{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<center><h1>Bayesian data analysis with PyMC3</h1><br>\n",
      "<h2>Thomas Wiecki</h2><br>\n",
      "<h2>Quantopian Inc.</h2><br>\n",
      "<br>\n",
      "First navigate **down** and then **right** to view all slides.\n",
      "</center>\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# About me\n",
      "\n",
      "* PhD candidate at Brown studying decision making using Bayesian modeling.\n",
      "* Quantitative Researcher at [Quantopian Inc](https://www.quantopian.com): Building the world's first algorithmic trading platform in the web browser."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image('quantopian.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "cXVhbnRvcGlhbi5wbmc=\n",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.Image at 0x101bbfa90>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Why should you care about Bayesian Data Analysis?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "import prettyplotlib as ppl\n",
      "from prettyplotlib import plt\n",
      "import numpy as np\n",
      "from matplotlib import rc\n",
      "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'], 'size': 22})\n",
      "rc('xtick', labelsize=14) \n",
      "rc('ytick', labelsize=14)\n",
      "## for Palatino and other serif fonts use:\n",
      "#rc('font',**{'family':'serif','serif':['Palatino']})\n",
      "rc('text', usetex=True)\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('backbox_ml.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "YmFja2JveF9tbC5wbmc=\n",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.Image at 0x107543ad0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Blackbox models not good at conveying **what** they have learned."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('openbox_pp.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "b3BlbmJveF9wcC5wbmc=\n",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.Image at 0x107543b90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Probabilistic Programming\n",
      "\n",
      "* Model **unknown causes** of a phenomenon as **random variables**.\n",
      "* Write a **programmatic story** of how **unknown causes** result in **observable data**.\n",
      "* Use **Bayes formula** to invert generative model to infer **unknown causes**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Random Variables as Probability Distributions\n",
      "\n",
      "* Represents our beliefs about an unknown state.\n",
      "* Probability distribution assigns a probability to each possible state.\n",
      "* **Not** a single number (e.g. most likely state)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Coin-flipping experiment.\n",
      "\n",
      "* Given multiple flips, what is probability of getting heads?\n",
      "* Maximum Likelihood answer:\n",
      "\n",
      "$$\\frac{\\# \\text{heads}}{\\text{total throws}}$$\n",
      "\n",
      "* However: \n",
      "\n",
      "$$\\frac{50}{100} = \\frac{1}{2}$$\n",
      "\n",
      "* Clearly something is missing!\n",
      "* **Quantification of uncertainty.**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Moreover...\n",
      "\n",
      "* Consider a single flip which comes up heads:\n",
      "\n",
      "$$ P(\\text{heads}) = \\frac{1}{1} = 1 $$\n",
      "\n",
      "* Again, this doesn't seem right.\n",
      "* Incorporate **prior knowledge.**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import stats\n",
      "# set every possibility to be equally possible\n",
      "x_coin = np.linspace(0, 1, 101)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## $$ \\text{Express probability of heads as random variable } \\theta$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111, xlabel=r'Hypothesis for chance of heads', \n",
      "            ylabel=r'Probability of hypothesis', \n",
      "            title=r'Prior probability distribution after no coin tosses')\n",
      "ppl.plot(ax, x_coin, stats.beta(2, 2).pdf(x_coin), linewidth=3.)\n",
      "ax.set_xticklabels([r'0\\%', r'20\\%', r'40\\%', r'60\\%', r'80\\%', r'100\\%']);\n",
      "fig.savefig('coin1.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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Bz/AgjorlONVwAyN3FAD8ljuVIAhYrg7FcnXotL0nzf0GvFtRjGN11/C9uDW8ZEA0DywE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108925a50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('coin1.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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a8P6Vmxucroycg4diE2VMRCS/cH81nkteBdXw1qlV1uvY5/BzQuTqPKYAEkUR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\n",
       "prompt_number": 7,
       "text": [
        "<IPython.core.display.Image at 0x108911e50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ \\theta \\sim \\text{Beta}(2, 2) $$\n",
      "$$ P(\\theta) = \\text{Beta}(2, 2) $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111, xlabel='Hypothesis for chance of heads', \n",
      "            ylabel='Probability of hypothesis', \n",
      "            title='Posterior probability distribution after first heads')\n",
      "ppl.plot(ax, x_coin, stats.beta(3, 2).pdf(x_coin), linewidth=3.)\n",
      "ax.set_xticklabels([r'0\\%', r'20\\%', r'40\\%', r'60\\%', r'80\\%', r'100\\%']);\n",
      "plt.savefig('coin2.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DbF1YQ5YxbkakABYCRB6sf3QYv75UJbfvWpKIDTHLlQtENEcqQcAD8avk9t9a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\nxlgIEBERLWP/D7YkUBDmWbG+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108970550>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('coin2.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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G4nf09+LjhirBiUg0FkBEdN/+dOMs6jptAIBAjR+2LTVxyjupip+kwfrEVKV9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\nnS6iPfhXCYCIhVHna5UbD36/H7t378abb74pLzjLnMeO2Wx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       "prompt_number": 9,
       "text": [
        "<IPython.core.display.Image at 0x10710cad0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ P(\\theta | h=1) = \\text{Beta}(3, 2) $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111, xlabel='Hypothesis for chance of heads', \n",
      "            ylabel='Probability of hypothesis', \n",
      "            title='Posterior probability distribution after 1 head, 1 tail')\n",
      "ppl.plot(ax, x_coin, stats.beta(3, 3).pdf(x_coin), linewidth=3.)\n",
      "ax.set_xticklabels(['0\\%', '20\\%', '40\\%', '60\\%', '80\\%', '100\\%']);\n",
      "fig.savefig('coin3.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TtdPos1khQ8Zf6s7hfGcztmUWIiY0QnRMIp/FQoDIw/Vah/DeRSPO3GhQLgtSBeCp9BW4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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108999a50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('coin3.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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gIjdp6O3C102Xlfbji1ZDI/G/GM2/hxKWIyooFADQOzSAQ7XlUxxB5Hv46Uzk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\nRESq8//IZ5BqQqFFfQAAAABJRU5ErkJggg==\n",
       "prompt_number": 11,
       "text": [
        "<IPython.core.display.Image at 0x108959690>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ P(\\theta | [h=1, t=1]) = \\text{Beta}(3, 3) $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111, xlabel='Hypothesis for chance of heads', \n",
      "            ylabel='Probability of hypothesis', \n",
      "            title='Posterior probability distribution after 20 heads and 20 tails')\n",
      "ppl.plot(ax, x_coin, stats.beta(22, 22).pdf(x_coin), linewidth=3.)\n",
      "ax.set_xticklabels(['0\\%', '20\\%', '40\\%', '60\\%', '80\\%', '100\\%']);\n",
      "fig.savefig('coin4.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qavD+++/jww8/RGtrK0pKSvDaa6+hq6tLzfiIdCWVx7dPFtapjnPME8VVzM3y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\n+/wAh64yAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108986b50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('coin4.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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slo9PXvp3DIwOJzxGItI/wyZAdXV1KCgoQGlpKaqqquD3+9UOiUj37g704JNrF+Tjpzb+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\nSJDw28tf4OvALTy98cdYnWVTO0SiJcU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6beLjPvfcc3HjEQQh7us9dOiQfP9AIICjR49O\nSj6il5944gnU19ejra0NTqcTHR0d8Pv9kx47ejneY0dfn/I1R03VljO9VzO1KQA8/vjjk2K/cOEC\n9u/fj5qaGrS0tMBqtcLpdM7YzkQ0P4I0059NRKQ6t9uN1tZWvPrqqwCA48ePw2azyYW2REQ0N+wB\nIvr/27djG4BhEACC3r+k9CSMlzptqkh/VzLBC8HP7e65956Zec29SAN8ZwMEAOQ4ggYAcgQQAJAj\ngACAHAEEAOQIIAAgRwABADkCCADIEUAAQI4AAgByBBAAkCOAAIAcAQQA5AggACBHAAEAOQIIAMgR\nQABAjgACAHIEEACQI4AAgBwBBADkCCAAIEcAAQA5DxZ/pGpAF4MOAAAAAElFTkSuQmCC\n",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Image at 0x108950b90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ P(\\theta | [h=20, t=20]) = \\text{Beta}(22, 22) $$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Bayes Formula\n",
      "\n",
      "$$P(\\theta| \\text{data}) \\propto P(\\theta) \\ast P(\\text{data} |\\theta)$$\n",
      "$$\\text{posterior} \\propto \\text{prior} \\ast \\text{likelihood}$$\n",
      "\n",
      "$\\theta$: Parameters of model (chance of getting heads)).\n",
      "\n",
      "* Except in simple cases, **posterior impossible to compute** analytically.\n",
      "* Blackbox **approximation** algorithm: Markov-Chain Monte Carlo (MCMC) instead draws **samples** from the posterior."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import stats\n",
      "fig = ppl.plt.figure(figsize=(14, 6))\n",
      "ax1 = fig.add_subplot(121, title='What we want', ylim=(0, .5), xlabel=r'$\\theta$', ylabel=r'$P(\\theta)$')\n",
      "ppl.plot(ax1, np.linspace(-4, 4, 100), stats.norm.pdf(np.linspace(-3, 3, 100)), linewidth=4.)\n",
      "ax2 = fig.add_subplot(122, title='What we get', xlim=(-4, 4), ylim=(0, 1800), xlabel=r'$\\theta$', ylabel='\\# of samples')\n",
      "ppl.hist(ax2, np.random.randn(10000), bins=20);\n",
      "fig.savefig('wantget.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DSGrtVy7pRM8FW2xN/gMOZZP8FubOUuEdz1pFFVUjq0MAACBFUQwhqf3+rP2D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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109a22a50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Approximating the posterior with MCMC sampling"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('wantget.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "PyMC3\n",
      "=====\n",
      "\n",
      "* Probabilistic Programming framework written in Python.\n",
      "* Allows for construction of probabilistic models using intuitive syntax.\n",
      "* Features advanced MCMC samplers.\n",
      "* Fast: Just-in-time compiled by Theano.\n",
      "* **Extensible**: easily incorporates custom MCMC algorithms and unusual probability distributions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Linear Models\n",
      "\n",
      "* Assumes a linear relationship between two variables.\n",
      "* E.g. stock price between Gold and Gold Miners."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "size = 200\n",
      "true_intercept = 1\n",
      "true_slope = 2\n",
      "\n",
      "x = np.linspace(0, 1, size)\n",
      "# y = a + b*x\n",
      "true_regression_line = true_intercept + true_slope * x\n",
      "# add noise\n",
      "y = true_regression_line + np.random.normal(scale=.5, size=size)\n",
      "\n",
      "data = dict(x=x, y=y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(7, 7))\n",
      "ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Synthetic data and underlying model')\n",
      "ppl.scatter(ax, x, y, label='sampled data')\n",
      "ppl.plot(ax, x, true_regression_line, label='true regression line', linewidth=4.)\n",
      "ax.legend(loc=2);\n",
      "fig.savefig('synth_data.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A58+f59SpUynsjRAb18M/L5Xlxz8AxgMTKKYHl0BVVSUYic6zx3Q21YifB2uL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\n98eMRiNO1cRk2IuGI2Q7pfrEalswCP7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35crF1FzobgIENZ2iYcRL+uP34O4WXcvKocwh\nV4Zh0B+ZRqvMLF4ybGb6Z8Z5bIuTYCKRYCYUxO1wAgb/X7+XqFVheHSECpeLWpebADGSvls8uXPf\nhn5WzFh6PsTSxVeTKorbmn+F77zzDj6fjzNnzmz6tmli+7oyPMCNxCyqpjGRThAduM4TWVwAmytc\nTM/5UU0mdF2nybr2VNdsKEjvhI+krtNaWcVDLfnvTRmPx7EYCko0SofPm/cyh/VS7rvluNaOKvk2\nFZjlwugtdJsJJpO40xrxShMqkDApROJhagw3igKzyeiGf16j1cF0YgZNUzEMg7oyvW0g1i+rj6Ie\nj4fXX399s2MR25g/FkE1Z1YbKorCVGIuq+ftbvZg8ZuZjIZwWGw81LL6iknDMLgwdIPU3b0ivVE/\nNr8Fzwb6+N3vqzs3GZoZovNOL98b6MOSzn+Zw3ooisL+6ma8ET+qWcMcS7OnrWXTf+5iXv8wht2c\nSb02C/3DY9RVNgFgQSOBvlCu4cjD3rZ7W9vRRlWm4xEqNSv728rztoFYv6LoLC+2P7tqIsi9ZGHP\nYSqwrb6JNpqyOnZubo451WD+8qqaTMzEIuRrLDg5eB3HZ//ODwavoK00NZuHMof12tPioSHkJhyN\nUt9SveVdMdIsnb6utjsglgSbmcbqGhyTM1jiKZwmK4+25qfHye7m/I/0RfkoeGd5UR4O7uggOnid\nmUQUIxqnrXlz9mO02+1Y0/c2PtFTadzO3AueDcNgcmYawzCor6kFvw/jkw+ouv4J1Tl2c9hqVU5X\nwVZIdrjq+PLuYiE9meZAo4d6p5vR4Aw2q4mOhx4vyTIXsX0VvLO8KA92q41v7j5Iz40+/HYLX8Um\n8d8K88TOfXm9KKqqylM7HuLSxB2Shs6OiiraG5pzeg3DMPjLrSuMK3HqJofQrn9Blf8O8GAXAwDD\nVoma524OpaqjoRm72cL0XJgqRwXNtZnNw6VsQRSrgneWF+XDNzHGpDmFpmbuDY6m44xPTdFUl98N\ntmvcbr7tXv8y+dHJcdQxL9+48TlVM+MrHpescKE88jSWR7+b1zKHXNwaG+LK9Bg6Bh2V1Rxq312Q\nOBZrrK6lsbpwI+HNFE8k+Ouda0wno1RqZh5v2UW1JPiSVhSd5UV5SBv6krIIRVVI6sWzpN1IJTGu\n/IWav/5fNAYnVz7wbpmDdRPKHHIRDIXoDY6jVlpQgNupEDX+cVrzuAhILNU7MkDAYqBZbMSAz0b7\n+b6zOOpWxfoUVWd5sb211NRz4/YEcVtmJFgR1WnxbG4n8mwY8SjG5f8X47M/Q2SWFcu37ytziMZi\nzM5MU+N0Y7Vu/UgwHIuiWO79CWdaN62yUnUNs6Eg4egcde5qbAV4P6Ugpqdg0ZaqUpdY+mTbNLFl\nrBYL3+o8wOBkZoqxY3dTQTdpNiIBjC/+A+Or/4T4yiUbRsdBtCefX1LmMDrl5xP/HbCZ0SZ9HG3Z\nTc0WF6XXuaswT/lI2TNp24glaahZXww3Rnz0RfxoFhPa9Ahf9+zZ9luurUe91Yk/PrVQl1gvdYkl\nL+sk6PV6OXXqFC6Xix/+8Ie0tbXJNmoiZ1aLJe/F62PTU9yazSTW3dVNNFbXkEwmuXjnGjPJGJUm\nM4+17Lq3r2ceujn0TQ6j2DPlB7rdjNc/xNe3OAlaLBaOefZywz+MDnQ0eta9AOVqYAytIjP6S9tN\n3Jgc5bHK0myJtJn27PCgjsJULIJdM3PA01HokMQGZZ0EX3vtNf74xz/yi1/8gldeeYUXXnhBkmCR\niycSfOG7STAVx6lZecSzE5t17X6ApSQYCXPRP4Biy4yGLvoH+LbFwg3/KNNmHcwWwsBnI/182121\nZjcHw2RBzaLMQce473FhuCoddJs7+XKon0sTgzinx3nEsyvnPTkNY+nK1/vfn7hnd7OHwi8/EvmS\n03Soa1EXbimRKH5f+m7hN6XApBEnxZdD/XxtV1ehw9qwGyM+hudmMSsaLtW8kAABsJqYDgeJpBOZ\ns9swqPEPsevKJ+iTvhVfM2GxMbDrYcKdj/NEFn0b2521XJ2bRDWbMBIpOqoa8vDO1udL3y0mTEnQ\nFOZI8KXvVlZb0i3W6aihPxlENWkosSQ7m7NrqCtEqcs6CXZ3d/PLX/6SUCjE6dOnpUSiBGQSwb3P\n96HVtvcqET7/ON6o/27n9BQjk5NYnJWo1rv3xRIpXNWV1CViWO58zu41yhyiNgf9ex9jqOMAaZOZ\nqlR29yj3trThnrIzG4tS63ZSX1Wdj7e3LqF0HEz3Vt2GUvGcX+Ng2y7qJv1EkjEa6qrlfqAoG1kn\nwddff51z584B0NbWxs9//vNNC0rkh8tsI2zEForRq0ylPxU6HQujmhatiKy0s9daw2BsFoCHXHW4\n71zC9emHMDO28gvdLXO4U1nP7VQY1aSRTqRoc2e/WrWptj7Lzdw2l1OzEuXevU2naX0rO5vrCr9S\nV4itltN06PHjxzcrDrEO4zPTXJkcRjd0Olx17Gxa2nbpsGcXqq+fQDKO02ThYU/2HcbzwTAMPr19\njbF4CIuqcbihfc0ial3XuTJ8hzk9Sb3dScd9u724LXbSkRCalhn52HSFvW0d7E3EMmUO//V/YkRm\nV/4B95U5HACc/jHC8Rg1tU6aapaPbzIwy6XxOyT0NC12FwfbdhXN9l+HPbv4cqifcCqOy2TlsCc/\ne3IKUQ6yToIvvPDCkseKovDHP/4x7wGJ7MTicS5O9IPNAihcDo9TOWNbkmRMJhOPdu4pWIzXRwYZ\nVeMoFVYSwCej/TxfVbNq8vj09jXGtASKojAcDKPr+pLk3tHYQsQXY3gugFlVOVzThPHx/1qzzGG1\nbg5t9auP5wzD4OLITdIVFsDE7XSIirGhotm42WqxbLgvnxDlKuskuHiz7J6eHnp6ejYlIJGdmWAA\nw2peWNGnmk3MzEWKaruqSDqxJOHFFZ1UKrXqysWJRASlIlN6oJlNjM0F2XlfY+EDnp10LSpzMNZZ\n5pCtZDJJFP1eZwpNI5TM/b6bEKL4rKtY/qmnnuLs2bP5jkXkoNrlRp32YdxdGZlOpKh2VRY4qqUa\n7G4GAyE0c+Y0q1Ksay7dt6omYkseL12oYkwMrlnmkO9uDmazGQfaQiMoPZWi1iULR4TYDrJOgouT\n3uzsKvdcxJawWa0caezE6x9Gx6DT2VhUo0CA1voGUkaa0cgsFkXjQOfay+4faerg4vBNYkaKGnMF\nB9rbMAwDfNfQP/kA7vSu/GSbA+WR7+a9m4OiKBxr38fl0UGSRpqWyvo1p1CFEKUh6yTo8XiW/PvV\nV1/dlIBE9hqqamioqtmU1w7PRZgJh6iqcOB0rD+hdDQ000H2rYzq3FU85348k/gw4OYXmeQ3dnvl\nJzlrUB57BuXgN/LazSGRSHBp+DbhdJIas42v5bHt03x3dSFEYa2ZBE+fPr3s13t7e/nZz36W94BE\nYfWPDXFh8Aa+eJBqp4smu4vHaz3sqNu6YvD5bg5GlmUOyt4jBObm+LT/CnPpJFVmG0fa9mx4E+jP\nfDeYNKVBhaARAV8/h9qyW3mZTqeZm5ujsrJySeeMQDjExeFbhFNx3CYbX2vbQ4V9+aa/hmHwxcAN\nJhMR7KqZh1s6pH5PiDxbMwl2d3c/8IlVPsVuP6lUiv/yfsHF2WFC8Tmc1VUESVKRinFtevSBJHjZ\nd4v+0BSqorDX3cSe+/YDNQyDgfER5lIJGhxu6lcYsfr841yaHCJt6LRqFg4HRzE+z3RzWNF9ZQ4A\nn4/eJmpTUbASwKBv9A6PdWxsZexsMg53axIVRWEmEc3qef7ADH8duUnSpGJNwVOtDy3s6fnl6AAx\nm4oJOxHgq5EBDjW34w/OUGm1U1997//JOzTAkBJFsWnE0flk+Cbf2yNte4TIpzWT4LPPPrvs171e\nb96DEYVzdXSQcSOGyWmHdIxQMkat2UlS1x/YR3J00k9/IohamSm+90b8NITcSzZv/nLgJoNGBFVT\nuTkxzROpFC33JdJkMsln/jvYtTS7b35Fe/9XGKu1AlqlzCGaTsLC+k2I5qHFjcNkZnbRe3eYLMse\nZxgGN0aGSOgpWtw1XBq/g1FhxQSkLdA3McQxZ2a7urieYnEvnqlQkP9MXcGwmUmHU+yPhNjb2g5A\nOBVHURft+JMq/R1/hCg2Wd8TPH/+POfOnUNRFAzDYGhoiI8++mgzYxNkLrDjU1Ok9EwSWTy1lk9x\nPYXD6UAbm8VmshKOJ8CiU2k1scu1dCeRSDKGatIIRyKMzEySxIBAhOcOPYnFkkkUw7FZ1LtdCVSr\nmcHQ1ANJMDrho/vK/9Dmu4qmp5cPLMsyhzpLJeNGpiQjndapt218pexjO3bz+fAtwqkEVWY7h9o6\nlz2u52YfU+Y0iqLQPzpNOhrHZL03bZky7m2vXW9xMKhHUFWVdFpHT8Ux2zLToZrZxPXgBHvJJMEq\ni52xeBT1brsp9zbY8UeIYpN1Ejx37hw/+9nPOHfuHM8++6zUCW6RT/qvMKokUFSFa9fH+Nbubkym\n/LeBbHFU4ZsK017XxMTsNA1BjcedrXQ2tFB3376Yje4arvomGJmdwnDYsKR0jCoH3tE7HG7PjNI0\nRWPxWMyk3Eve82UOldc/oTNPZQ6Pd+yhb3iAuXSCWpsjL+2aKux2vr67e9VjUqkUY6k5LJZMglJs\nZpRQFD2VRjVp6Mk0Hse9+A937KZieJBgKka1vYKZOjtj3Ks5VBb1ctjT0kZyKI0/FsGmmnhY2vYI\nkXc5XU0PHDgAZOoE5/cRFZvHPz3FiJJY2CIsYoPbE6N578cH0FzbwJPASHiWfc217Hu0bcVk66yo\n5Knm3QxOjaNhotblxGTSMl237zpUt4NP/YMYFg17EvZ5dmIMXs25zGE2GCA25aehunbVUbCmaVkv\nWsknVVXR7svju5tacFkrCMTmqK1yLIyAdV3H5x/DYjJxuHEnZrOZmWCAibGb6FYzejLFvkXdKBRF\noXuLt7oTotxknQQNw+D8+fMAvPvuu4RCoU0LStxlGCiL7kllpqI378c11zbQXJvdKtAal5vDDe1M\nmlKZKchkisZFI54ddY00uGuIRMI4J27B//439BzLHHp9/dyMz6KaNCr9w3xjZ9fCdGuxUFWVgzUt\nXJoZBZOGK62wZ2frA3EahsF/37hMwJr5Pd68NcG3d3VT7XLzHXMX/uAMjqoKat3SokyIrbRmErxy\n5Qr79+/nnXfewefzcfToUd566y3pIpFHAxOjXBobZHBmAofNzp7aZh717KKuppbaqVGmVR1FUbBG\nU7TvbCx0uAue7NxH3/AAMT1JvauOzoaWhe8ZqSTa1b/gzKHMQdHunY7xeJzr4SnMd+8rztkVbk2M\nsL+1Y7PezrrtamqltbqeeCKB0+FYduX02JR/IQECxOwag5Pj7GpupcJup/1umcTY9BSBaISaysK2\nZypG/WPD3AxMoKCwt6ZJNiwQebFmEvyXf/kXhoaGePbZZzl+/Dgul0sSYB6FwmEuzY5wZ26aeJWN\nqJ7GmgyhDQ/w5M59HHuoG9/EKGnDYEdzfVGNhJabgjTi0Uw3h89yL3NYLJVOY9yXS4q527nVasW6\nSl2iioqh6yh3F7kYhsH9qfLmqI/eiB/NbEKfnOThROyBLhrlajIww6XQOJotc8n6YmYEt60C96IV\nyUKsx5pJ8J133iEYDPLBBx9w8uRJAJ5//nmOHz+OYwM7iYiMUDQCFhNJMisIFVUlnk4RudsYVVEU\nmmrq+dJ3i/7gJE6TlUc8u9bcg3MzDYyPMBWL4DLb2N3cmpmmjQQwvvgPjK/+H4ivUk+3SpnDYhV2\nO81aBX49mbnvFkvR7ll7qnYyMIMvMIVF1djT2FrQ/6fFGmprqZ8Zx68kURQFZ8ygo61lyTEDoSk0\nq4lUKkUwFOSLwBzt9U1SkwsE5yJolkV9JK0mAnMRSYJiw7K6J+hyuTh+/DjHjx8nFApx8uRJTp8+\nzZUrVzY7vm2vxlWFaXoYq6IyZxgoukGFzUKV+d4uIl/4bjGhJUBTmCPBl75bPFGg1jnXhu5wJT6N\nZtIYioVJecfYO3YVo/d/YKXavHV0c1AUhaO7uxgYHyGp67Q21K24s8q8ycAsH4/3Z7rMp2Gi38u3\n9xwqiiQy/37GpiZJ6zotbfUPLPRRUUglk9yaGIVKK0E9ySf9Vziyq6tAURePGocLxiZgPhHGU9TW\nuwsblNgWsl4Y4/V6ef/99zl//jxdXV38/ve/38y4yobNauWplt1UqRYGpsZx2itptVbz8KJpxlAq\nBtq9C2YwtXYbn3giwc2JYXTDoL2mIS/bbSWTST4f7WfOquGJR9h/+xLNQzcwVpqm3GA3B0VR6Lyv\nUfBqRoLTmQR417SRIBaLYV8jeW4VRVFW7d6+v24H/37tc6i0QkqnwVHNsB4jEAzidpX3iKfK6eLx\nhIebsxNgwN6GViorKgodltgG1kyCZ8+e5dy5czidTl566SX++Mc/4irzP8h8q3K6eMrZzVMPLV+T\n5tSsRLnXM89pWn1PTF3X+e/+PqL2zP2nO0OzfMuzF2fF+gvIDcPgv2/2Yp0e5MjodTqmV1nssknd\nHNZiUdQlW/qpab2o7qGupbmmjiPNnfTOTWJ32DCZNFLJje98s10019bTXLvyhwgh1mPNJDg7O8vv\nf//7JV0kxNY67NnFl0P9hFIxXCYbj3hWr4ebmpkmYlWYHzsaNhPDM1PsW2cSNAyd0Ff/w6EvPqR6\nZnzlAzepm0O29rS0MXXTy1gqgslQOFzvQdO0tZ9YRPY0tzF2O0TcpKGn07QotrIfBQqxmdZMgrIS\ntPCsFgtP5nAP0GqxYiTTYM2kQV3Xsa4jGSzu5lA5M8aKKXSFMoetpqoqx/Z0k0ql0DStKO4F5spm\ntfKdXQcZmfZjUrUHtpoTQuRX4a5YYtO4nE72Bmq5EpoAFZrUSjoaW9Z+4l1Zlzm07EY98tyKZQ6F\nshnbym0ls9lMew6/LyHE+pX21UKsaH9rB7uTO0in09hs2W28nG2ZQ7q9G9OTz6O2bqxVkcivr+7c\nZCAyjYbKgdoWOiWRCrEmSYIlKhgJMx6YocJiXbHhrdlsXrZObmJ2Gq9/mLSh0+6sYZfdhvHZh1mX\nOWhZljmIrTM4McrtdBit0oYBXAqM0uB0U7mBxVBClANJgiVoOhigZ/Qmhs2MHk0zFQlyqH13Vs+N\nx+P8dew22M04ZyexfPEB6ZGbKHnq5lDKJmanuTQ+SFxPs8Pu5uH2XSiKwnQwgC8wiUlRi6oAf7G5\nZHJho3UAxWIiHI1KEhRiDZIES1D/zDiGLXMhVk0a/ZFpDi4qDVhNIBSkJjTKrs8+p378zsoHFqjM\noVAMw+DiyC2MSiugckcP4xgborbSxf+M3kS5+/89fquP7+x9OKdFN6lUik/uXGMqMYdNM/NY806q\n87zTSZPTzfVR/0Kc5lia2lbZjFuItUgSLEH3L0HJZkmKYehw8wtq/vo+X5sYWPG4qN3B5J4naf/m\niwUpc1jLxMw0odgcdU43boczb6+bSCSIq8ZCb3pVVQmn4sQC0wuJBSCg6YTCYVzO7H9238gAfi2F\nUmElCnw60s8P9h7OW+wAVS43R/VOBmb9aIrC3rbWkl8gJMRWkL+SErSnfgfjg1dJ2c2kEykOVDWv\nODJZXObAzNgDmzbPC7lqubXnMUY9e2hUKugowgR4fcSHdy6zwbQxPM7XGjtpqKrJy2tbLBachmmh\nva2eTFNf5SISj2GkF42yU2msORbgR9NJFFVZ8ngz1FfVUJ+n/w8hyoUkwRLkqKjkO53dTAZmcdTY\nqVpmai2Xbg5DnY/yZXUtqsWMEkuyu7kwLWpSqRS+yXEUoK2h+YG9NW8FJ9Dsd0dlVhM9N71072hn\nR009dmt2K2BXoigKX+/Yz+XROyQNnR3OWnbUNaDrOv6bQcbSEVQdDtXuWLVbxHLqbA7GolML9+yq\nzXb6fP0MRmbQFJWDda2yE4oQBSJJsETZrFZaGx7sLbiebg7tgG16irlEjPr6KhwFWEyRSqX4rxuX\niVZoGIZB/3U/395z6L5EeG805RsdAasZLTHD9dsTfLNt34bjrrDbH9iUYL4AP5lMomnaqt3tV7K7\n2YMxAv5YGLtmwl1p43J0EtVuJgV84r/DMw5XzslVCLFxkgS3CWPWv2aZg66oDLXsYmLXER4+eAzb\nootuY83Wr/zs891mLBrEomq4VQtzdhWFzKgsbIPhyQk8DfdGpfuqm7gcHCNppAkbSTrudrJP280M\nTE3QXdG5abFudEXoQy0e5ptHeYcGUE33dvAxLBqhSHjVJGgYBtdHBgkmY7jNdh5q8ZTkjjhCFBtJ\ngiXOmBjE+OQDjOufwCplDnda93Fr/2PEKjJTp1fHBjncvnpPv810e3yYG8kAmlVlDp2B4Tu4mhuY\nv64bhoF63y40nY0t1FQ4mJydIelMUrFoCrSU8kGdw8UN/zTq3bZA5rhOlWv1tkCXBm8xoIdRVZWR\n+BzJoRQHPDu3IlwhtjVJgiXIMAzwXUP/5AO407vygYvKHC7d9qJV3EsaSUPfgkhXFohHl9S17150\nmQAAF9lJREFU2atcOOZSRCpNGIZBXdJEyzJth9xOF26ni4iR4nYygKJp2GM6uzpLZ3eUhqoaHk0m\nuROcQlMU9rd2rLmScyIeRr27F6yqaUzEwhzYimCF2OYkCZaQ+TIH/ZMPYOz2ygcu082hyVLJhJFC\nURTS8RTN1YVZ/DKvylbJQCiEdnda0KGY+M6+Rxib8qMoKk21datO9x1q20XLzDTxVJKGHTVFWcC+\nGk99I576B+/prsSmmIhx74OLVZU/XSHyoWj+kk6cOME777xT6DCK0v1lDiu6281hsNrDWCyMdWSQ\nruZ2rBYLR3bu58aIj6iepLHWXfDViB0NzcQSCUaiASyqRndrB6qq0pJDYqirLp9ygIdbOrjou0Ew\nncBtsnLI017okITYFgqeBC9cuMDg4CAXLlwodChFJ5cyB/WJH0LnQQb943wVHEU1m4AkwYGrfOvu\nKsu9rcV14dzX2k72DaLWLxwJA+CoLN2db1yVDr6/75ElTYOFEBtX8CR49OhRjh49yj/90z8VOpSi\nkW2Zg9F5CO3Icyg77i1wmZgL3k2AGdPJaF4unMFImNHANDaTmbb6ppK5EH/af43BZAhFgVZTJY93\n7iuZ2JdTyrELUYwKngTFPdmWOYx49nLroUfpaj1I033TmnbNjJGOLVwsbappwxfOmWCAj+c37I6l\n8YcDPJ5Dk99CGZ30M0QUsy2zw8tIOs7o1OSyC26EEOVJkmARyKbMQdfMDLR3MbD3UWIVLvR4gkqb\n/YHjunZ0EOq/gj8xh0018WjL8svoDcPgyvAAgWQMp8nKgdbOFZPlwKz/3obdmsZgOMCjur6uwvGt\nlEwv7aygairJTdqyTAhRmiQJFpju7cH48O2VD7hb5mA6/D3CY6NEojNokTgHa1pwLnOPS1VVju5e\ne/H85cFb3L5bd+ZPJ0jeuckjHcvXDar37Tg6X9Be7Bqr67D2jxO3Z1agWqIpmprrChyVEKKYSBIs\nMKXzEIbJAqnE0m8sU+bwSOdDPJKnnzuVmEO1ZEZJiqIwGY+seOyexh2M375CzKaiJ9IcqG4piSRo\ntVj4Zud+bvvHQFHo7GzMefNrIcT2JkmwwBS7A+XgNzG++L8zX7hb5qDsPYKibd6vx6aaCXNvarBC\nXbnOzm618d3dB5kMzFBhseXURqjQbFYb+1s7Ch2GEKJIFTwJer1ePv74YxRF4fTp0xw7doyjR48W\nOqwtpTz2NIbfh/r4M9B5EEVZ+V6bYRgM+seIJhM0uaqX7SCRjcOtnfx18DrBZByHycrDayQKk8n0\nwCIcIYQodYphrLThZPH57W9/y09/+tNCh1FQn9++js+YQ9VUjFiSo407qa+qLnRYG3Z7bJjJeASH\nZmHfjvaSmG4VQpS+4l7eJ5YwDIPB6Czq3RWPis3MnYC/wFFt3LXhQb6KTDBGjOvJWT4fuF7okIQQ\nZaLg06Eie4qiPLBSU1uxV3zpGI8G0cyZFZyqquKPrrxIZ7PEEwnGZiaxaGaapY5QiLIhI8ESc7B2\nB+lYAl3XsURT7G1sLXRIS6TTaWaDAVKp5Yv9l3P/ZtC2TVwQtJxoPMZ/3u7lUmySvwaH+ez2tS39\n+UKIwpGRYInpbGyhwekmEo1SW1WNpmlrP2mLzIaCXBi+QcwEpqTBk807aahae5Prg83t/GXwOrPJ\nKJUmCw/v2L2pceq6TjqdXug80e8fJWnL/ClomsqdWJCuaBS7/cHNCIQQ24skwRJUWVFJZUVlocN4\nQN/EECm7OXNSmaF3wsd3s0iCFXY739378JZsDn17YoSvJodIKwYNmp1ju7tRtsGUshBifSQJlpl0\nOo1/dppKm33ZHWcWuzU2RH9gEgXYX9vMjrrV2xxNBwMELAYOewWappLKsXHvZifAdDrNl34fJocd\nDZgxDK6N3KGzvpnBgWmSNhPptE67xSWjQCHKhCTBMjIXi/LfA1eI2zT0qRTdzkYqLZaFRLevbgc1\nLjcAEzNT9IYnUO9OE342PYzb7sBRufwI9NP+q0yko0xF45hjIdqcteyqrN2qt5aVZDKJYbqXaBVF\nIWHo2K02vtPZLQtjhChDsjCmjFybGCJhz3SV0KxmLg7d5OLUINNaiiktRc/oLWLxOACB6NySlkyK\n1cRsJLTs64bCIXypMA31dbRYHFQaGnVxla7Wzi15X9my2WzUYlt4bMRT7HBlErXVYqG9sUUSoBBl\nRkaCRebGiI874Sk0VLrqd9BYnb/RlH7ftgiReBSH5d49O8NmYio4y476RmodLvQxP6o1c4qosRS1\nje5lX3fxy7rdbtxAg2n5Ywvt2K4uro0OkkSntbGOOndVoUMSQhSQjASLyNiUH+/cJEElzdXZMd7r\nu8A130DeXr+jqh5imf1C9bROh7MGI5Ve+L6RSOKyZ6Y7a1xuHqvegTupUpXUONLYgX2Z1k0ALoeT\nHVolejpzD9ASTdG5xv3DQjGZTBzw7OSwZ7ckQCGEjASLyUxsDtWscXt4hFSlBaxWPp0dwma10t7Q\nvOHXr3VX8Q31IcaCs9jtZto6m7g+Mkh/aApVga6aVpyOe4tlWusbaa3PLpk9sXMfo1N+kulMuyLp\n1iCEKAWSBItITYWDlH+SqJHCjAUlpVNRVclENEQ7G0+CAFVO15JNt/fuaGcv7Rt+XUVRaKlr2PDr\nCCHEVpLp0CLSWF3LI+5mKtMqNl2h1VGNpqlUqGt/VonFY9wc9dE/OoSu51aaUCgltHe7EGKbkpFg\nkelobOH/sNn5fHSAuJ6iLmVif1vHqs+JxWP8120vSbsJwzDw3Zjim3sOFW0nhqlAgE9HbxFJJ6k1\n23myfS82q7XQYQkhypAkwSJU567maXf27ZF8U36S9syvUlEUZi06/ukpGmrrNivEDfl87DYJuwkz\nJoKAd3SQRzseKnRYQogyJNOh24CCsmRq0dCNotpT9H5xPbnkcUzPfrPtfEmn00zPzpBMJtc+WAix\nbclIcBvY2dSC7/okIVum9MGjVFBbxI126y2VjBtJFEUhnUzR5NjanWWCkTAfD14jblFQRtMcaeig\nuVaK5IUoR5IEtwFVVfnWnkNMTE9h0jTqqtfetLqQnujch3d4gGg6Sb2rno48lH/k4urEMKkKMxqA\nycSlSZ8kQSHKlCTBbUJVVZpKZMsvVVXp9uws2M+/f2Pv1P1b6QghyobcExRlx+OsQU9k7kOm0zqe\niuLc4k0IsflkJCjKjqe+EavZjD8cwGGz0t7YUuiQhBAFIklQlKWGqpqsut4LIbY3mQ4VQghRtiQJ\nCiGEKFuSBIUQQpQtSYJCCCHKliRBIYQQZUuSoBBCiLIlSVAIIUTZkjpBURIGJka5NTsBwJ7qJjz1\njQWOSAixHchIUBS9mWCAS4FR5qwKc1aFL2aGCYSChQ5LCLENSBIURW92LoxiuTdpoVhNzM6FCxiR\nEGK7kCQoil6NwwWJRY1346nM14QQYoMkCYqi53Y4ebymFXdSxZ1QeLyuDWelo9BhCSG2AVkYI0pC\nc20DzbUNhQ5DCLHNSBIUG6LrOhdvX2UiHsGsqDzS1EFTdW2hwxJCiKzIdKjYkGsjg0xoSZRKK6kK\nM5+O9mMY0qldCFEaJAmKDYnqSRRFWXgcVwzS6XQBIxJCiOxJEhQb0ljhRk/eW7lZo1oxmWSWXQhR\nGuRqJTZkR10DumEwEp7Bomrs72grdEhCCJE1SYJiwzz1jbKNmRCiJMl0qNgSyWSSyNycLJoRQhQV\nGQmKTXdtZJC+wDioCnVY+PpD3aiqfP4SQhSeJEGxqWKxGN7gOOYKKwCzhsHN0SH27Fj93uHI5ATD\n4RlMisr+pjZsVutWhCuEKDPycVxsqngyAaZ7p5miKKQMfdXnjE5P8snMEGNKnCGi9Axc2ewwhRBl\nSpKg2FQuh5OqlLZwL1CJJWlx16z6nPHILOqirhEzRoJEIrGpcQohypNMh4pNpSgK39jdzc2xIVKG\nQWtLLVXO1TtA2FUzRtpYKMI360jtoRBiU8iVRWw6k8nEvtaOrI/f09LGTH+EsXgYq6pxqKFDFtII\nITaFJEFRdBRF4Wu7ugodhhCiDMjHayGEEGVLkqAQQoiyJUlQCCFE2ZIkKIQQomxJEhRCCFG2JAkK\nIYQoW5IEhRBClK2iqBP0+XycP3+eAwcO0NfXx/Hjx3E6nYUOSwghxDZXFEnw1KlT/OlPfwKgu7ub\n1157jTfffLPAUQkhhNjuCj4d2tfXR1VV1cJjp9PJhQsXChiREEKIclHwJOjz+R6Y+nS73Vy5Iu1z\nhBBCbK6CJ8FAIFDoEIQQQpSpgifBqqoqQqHQkq9JYhRCCLEVCp4EPR4Ps7OzD3x9//79BYhGCCFE\nOSl4EuzqWtoyx+fz8dRTTxUoGiGEEOWkKEok3njjDc6ePYvH4+Hy5cu88cYbhQ5JCCFEGSiKJNjV\n1bUwInzmmWcKHI0QQohyUfDpUCGEEKJQJAkKIYQoW5IEhRBClC1JgkIIIcqWJEEhhBBlS5KgEEKI\nsiVJUAghRNmSJCiEEKJsSRIUQghRtiQJCiGEKFuSBIUQQpQtSYJCCCHKliRBIYQQZUuSoBBCiLIl\nSVAIIUTZkiQohBCibEkSFEIIUbYkCQohhChbkgSFEEKULVOhAxAbE08k+MJ3i1AqjtNk5RHPLqwW\nS6HDEkKIkiAjwRL3pe8WflOSmE3Fb0rype9WoUMSQoiSIUmwxIXTiVUfCyGEWJkkwRLnMtnue2wt\nUCRCCFF6JAmWuMOenTTrVioT0JS2ctizq9AhCSFEyZCFMSXObDbzeOfeQochhBAlSUaCQgghypYk\nQSGEEGVLkqAQQoiyJUlQCCFE2ZIkKIQQomxJEhRCCFG2JAkKIYQoW5IEhRBClC1JgkIIIcqWJEEh\nhBBlS5KgEEKIsiVJUAghRNmSJCiEEKJsSRIUQghRtiQJCiGEKFuSBIUQQpQtSYJCCCHKliRBIYQQ\nZUuSoBBCiLIlSVAIIUTZkiQohBCibEkSFEIIUbYkCQohhChbkgSFEEKULUmCQgghypYkQSGEEGVL\nkqAQQoiyJUlQCCFE2ZIkKIQQomxJEhRCCFG2iioJnjhxotAhCCGEKCOmQgcAcOHCBQYHB7lw4UKh\nQxFCCFFGimIkePToUY4fP17oMIQQQpSZokiCQgghRCFIEhRCCFG2Nu2e4Lvvvsvg4OCK3z927BhH\njx7N6TWdTie//e1vNxqaEEKIbeTIkSM8+eST63quYhiGked41m3fvn1cvXq10GEIIYQoEzIdKoQQ\nomwVRRL0er2cOXMGRVE4ffq0lEoIIYTYEkU1HSpEMfP5fJw/f54DBw7Q19fH8ePHcTqdaz7v5MmT\n/OY3v9mCCEU5O3HiBO+8886qx6z3HC4G2bw/r9dLb28vwWCQy5cv8/Of/xyPx7Pqc4qiWL5c5HIC\nluLJmkvM6zlZC+3UqVP86U9/AqC7u5vXXnuNN998c9Xn9PT08NFHH21FeBuW6zl3/vz5JY+feeaZ\nzQ5xQ9Zzfrrdbnw+H88880zRnp+5bDaynnO40LJ9f6FQiN7eXn784x8vPO/ll1/mz3/+8+o/wBBb\n5m/+5m8W/h0MBo2TJ0/m5dhikW3MwWDQOHfu3MLjnp4e4/vf//6mx7cRvb29xokTJ5Z87Yknnlj1\nOcFg0Ojt7V3zuGKRyzn31ltvGefPn184dvFzi1Uu7+/MmTNLHv/iF7/YtLjyZe/evat+fz3ncDHJ\n5v0tvo4EAgFj7969RigUWvV5RXFPcDU+n4+zZ89y4cIFzp49SygUWvFYr9fLu+++y9mzZzl16hQ+\nn28LI11dX18fVVVVC4+dTueKn2xyObZY5BLz4OAgZ86cWXh84MABfD4f4XB40+NcL5/P98Cowe12\nc+XKlRWf09PTw4EDBzY7tLzI5fcXDAY5c+YMTz/99MKx86OLYpXr39S5c+dWvdaUovWcw6XkwIED\nS6ZL50fyDodj1ecVfRI8deoUr7zyysLWaq+99tqyxy0eCr/yyiu89NJLvPzyy1sc7cpyOQFL8WTN\nJeb1nqyFFAgEcjr+woULHDt2bJOiyb9cfn+9vb20trZy/vz5hQ+nxfSBczm5/k395Cc/4Xvf+x7v\nvvsu7777Lv/4j/+4FWFuqlzP4VLU2tq68O9z587xz//8z2s+p6jvCa5ndDE/H7x4dFEMF9dcTsBS\nPFlzjXk9J2shVVVVPTAyWOk9+3y+ok/q98vl9+fz+fB6vRw7dgyHw0F3dzcvvPDC2vdeCijX8/PH\nP/7xwjXF6XRy9OjRkvp9LieXc7jUvfvuuzz//PMLsxWrKeqR4HYaXeRyApbiybremHM5WQvJ4/Ew\nOzv7wNf379//wNfmF1XMjyKCwSDvvfdeUY+Wcvn9eTwePB7Pwt+W0+nE5/MxNDS06XGuVy7vLxgM\ncvr0aX7+85/z5z//meeee66oZpXWK5dzuJRduHABj8eT9TWlqEeC22l0kcsJWIon63pinj9Zc90+\nrxC6urqWPPb5fDz11FNLHldVVeF0Oh9YJfnLX/6SH/3oR1sS53rlen7ez+VybUpc+ZLL+7t/KvuV\nV15hcHAQr9f7wHlQ7Bafl2udw6Vo8fuDzOyh2+1eeK8ffvghzz777KqvUZAkmO2+om63e9uMLnK5\niJbiyZrL+4P1nayF9sYbb3D27Fk8Hg+XL1/mjTfeWPje6dOn+frXv74k2YVCIf7whz+gKApvv/02\nTz/9dNEus8/l9+fxeHA6nYRCIZxOJ8FgEI/Hs+RDaLHJ9f319PQs+XDmcrmKNgF6vV4+/vjjhc1G\nFu/LfP95udo5XKyyfX8+n48XX3xxyXPb2trWvK4UdbG81+vltddeW7Ly7MiRI1y8eHHF58zfMyzG\n0YXX66Wnp2fhBPz7v//7hSmlU6dOLTlZVzu2WGX7/nw+Hz/4wQ+WPLetra1k6um2q1zOT5/Px7lz\n5zh48CCXL1/mpZdeKuokCLm9v/PnzxMIBHC73QQCAQ4ePFjUMzFi/Yo6CQK88MILC0nQ5/Px61//\nmn/7t39beHz/6EJRlJIaXQghhCicok+CMroQQgixWYo+CQohhBCbpahLJIQQQojNJElQCCFE2ZIk\nKIQQomxJEhRCCFG2JAkKIYQoW5IEhRBClC1JgkLkwenTpzl79uySr504cWLFVj0ffvghp0+fznsc\nJ06c4L333tvQa6wU2/e///0Nva4QxUiSoBB5cPz4cc6dO7fwOBgMMjQ0tOJWW4qi5D0Gn8+Hoigb\n3qx7pdg2I2YhCq2ou0gIUSrmN5We7zTwwQcfLGzZd+LEiYUEcvz48SVdJs6fP4/P5+OVV14Blm4T\n+Mtf/pLe3l4gs/Hx4g2cT548STgcZnZ2ln/4h3/g6NGjnDlzht7eXj766KMHNo+fP761tZXe3t6F\nn7Hc68wLhUKcPHkSRVEeaGkmxHYhSVCIPHnppZd4//336erq4ty5c/zmN7/B5/Px0ksv8cwzz9DX\n18evf/3rB1otLWd+VPmnP/2JYDDIiy++uNC09syZMzz88MP83d/9HaFQiO9973tcvHiRn/zkJwwN\nDT2QAH/1q18tHH/hwoWFTeZXep15v/vd73juuef40Y9+hNfr5W//9m/z9D8lRPGQJChEnjz77LO8\n+OKLvPrqq0Cmv2UwGOTjjz/m448/BrKfUuzr62NoaIhTp04BS/v1+Xw+fvjDHwJkNUIbGhri+eef\nB5Z2V1nrdbxeL8ePHwcebEUkxHYh9wSFyJP5nnMnT55cSDpvvfUW3d3dvP766zz77LPMb9W7eMve\n+WavPT09C1/r7u6mq6uLN998kzfffHMhWUFmY/i+vj7g3n3A1cz3x7v/Z6z0OvOxdXV1LYwa548T\nYruRJChEHr300kv85S9/WRhBPffcc3z44YecOnWKnp4ehoaG8Hq9KIqCoig89dRTeL1eXn75ZXp6\nehYS0Y9//GN8Ph8vv/wyL7zwAm1tbQs/45VXXuHy5cu8/PLLnDp1aqG1GCw/0nz11Vfp6elZ+Blr\nvc58bK+++ioffvghL7/8Mm+99VbRNgQWYiOki4QQ29ziRtN9fX3867/+K2+//XaBoxKiOEgSFGKb\nC4VCC/cWAV5//fWi7wIvxFaRJCiEEKJsyT1BIYQQZUuSoBBCiLIlSVAIIUTZkiQohBCibEkSFEII\nUbYkCQohhChb/z+PrSKkMZIHrgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109f7e650>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('synth_data.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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YwF4gK9LdTiTBCyFWrbiWZOp07Ww3PyeTSdp7L+OPRbAbTDRWr0NVV0bPpjfg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\n7ZMPAO/k2gc6axA8fBwfN7VClW6fPnNBPzospdvWdTg4D+HmVaxgNCA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       "prompt_number": 20,
       "text": [
        "<IPython.core.display.Image at 0x1089857d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Linear Regression\n",
      "=================\n",
      "\n",
      "$$ y_i = \\alpha + \\beta \\ast x_i  + \\epsilon $$\n",
      "\n",
      "with $$ \\epsilon \\sim \\mathcal{N}(0, \\sigma^2) $$\n",
      "\n",
      "Probabilistic Reformulation\n",
      "---------------------------\n",
      "\n",
      "$$ y_i \\sim \\mathcal{N}(\\alpha + \\beta \\ast x_i, \\sigma^2) $$\n",
      "\n",
      "### Priors\n",
      "\n",
      "$$ \\alpha \\sim \\mathcal{N}(0, 20^2) $$\n",
      "$$ \\beta \\sim \\mathcal{N}(0, 20^2) $$\n",
      "$$ \\sigma \\sim \\mathcal{U}(0, 20) $$\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Constructing model in PyMC3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!conda install pymc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Conda package not available for pymc, attempting to install via pip\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Downloading/unpacking pymc\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Downloading pymc-2.3.tar.gz (13.1MB): \r",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
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       "text": [
        "  Running setup.py egg_info for package pymc\r\n"
       ]
      },
      {
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       "text": [
        "    build_src\r\n",
        "    building extension \"pymc.flib\" sources\r\n",
        "    f2py options: ['skip:ppnd7']\r\n",
        "    f2py:> build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c\r\n"
       ]
      },
      {
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       "text": [
        "    IOError: [Errno 2] No such file or directory: 'skip:ppnd7'. Skipping file \"skip:ppnd7\".\r\n"
       ]
      },
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       "text": [
        "    updatevars:gradlike: attempt to change 'dimension(nx)' to 'dimension(na)'. Ignoring.\r\n",
        "    updatevars:gradlike: attempt to change 'dimension(nx)' to 'dimension(nb)'. Ignoring.\r\n",
        "    updatevars:gradlike: attempt to change 'dimension (nmu)' to 'dimension(nmu)'. Ignoring.\r\n",
        "    updatevars:gradlike: attempt to change 'dimension (na)' to 'dimension(na)'. Ignoring.\r\n"
       ]
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       "text": [
        "    rmbadname1: Replacing \"index\" with \"index_bn\".\r\n"
       ]
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       "stream": "stdout",
       "text": [
        "    updatevars: \"character curterms(20)*24\" is mapped to \"character curterms(20,24)\"\r\n",
        "    updatevars: \"character septerms(20)*24\" is mapped to \"character septerms(20,24)\"\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    updatevars: \"character tokens(maxtok)*(*)\" is mapped to \"character tokens(maxtok,(*))\"\r\n",
        "    rmbadname1: Replacing \"index\" with \"index_bn\".\r\n",
        "    rmbadname1: Replacing \"len\" with \"len_bn\".\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    sortvarnames: failed to compute dependencies because of cyclic dependencies between x, nx\r\n",
        "    sortvarnames: failed to compute dependencies because of cyclic dependencies between w, x, nx\r\n",
        "    sortvarnames: failed to compute dependencies because of cyclic dependencies between n, nx, bin0, delta, x, d\r\n",
        "    \"object of type 'builtin_function_or_method' has no len()\" in evaluating 'len(ord)' (available names: [])\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    Reading fortran codes...\r\n",
        "    \tReading file 'pymc/flib.f' (format:fix,strict)\r\n",
        "    \tReading file 'pymc/histogram.f' (format:fix,strict)\r\n",
        "    \tReading file 'pymc/flib_blas.f' (format:fix,strict)\r\n",
        "    \tReading file 'pymc/blas_wrap.f' (format:fix,strict)\r\n",
        "    \tReading file 'pymc/math.f' (format:fix,strict)\r\n",
        "    \tReading file 'pymc/gibbsit.f' (format:fix,strict)\r\n",
        "    Line #1125 in pymc/gibbsit.f:\"      DOUBLE PRECISION INTENT(CACHE,HIDE), DIMENSION(2*ITERACNT) :: WORK\"\r\n",
        "    \tupdatevars: attempt to change the type of \"work\" (\"integer\") to \"double precision\". Ignoring.\r\n",
        "    Post-processing...\r\n",
        "    \tBlock: flib\r\n",
        "    \t\t\tBlock: symmetrize\r\n",
        "    \t\t\tBlock: logsum\r\n",
        "    \t\t\tBlock: logsum_cpx\r\n",
        "    \t\t\tBlock: combinationln\r\n",
        "    {'attrspec': ['intent(hide)']}\r\n",
        "    In: :flib:pymc/flib.f:expand_triangular\r\n",
        "    vars2fortran: No typespec for argument \"nf\".\r\n",
        "    {'attrspec': ['intent(hide)']}\r\n",
        "    In: :flib:pymc/flib.f:expand_triangular\r\n",
        "    vars2fortran: No typespec for argument \"n\".\r\n",
        "    \t\t\tBlock: expand_triangular\r\n",
        "    \t\t\tBlock: mod_to_circle\r\n",
        "    \t\t\tBlock: standardize\r\n",
        "    \t\t\tBlock: gammln\r\n",
        "    \t\t\tBlock: mvgammln\r\n",
        "    \t\t\tBlock: factrl\r\n",
        "    \t\t\tBlock: factln\r\n",
        "    \t\t\tBlock: normcdf\r\n",
        "    \t\t\tBlock: sn_like\r\n",
        "    \t\t\tBlock: rskewnorm\r\n",
        "    \t\t\tBlock: uniform_like\r\n",
        "    \t\t\tBlock: uniform_grad_x\r\n",
        "    \t\t\tBlock: uniform_grad_l\r\n",
        "    \t\t\tBlock: uniform_grad_u\r\n",
        "    \t\t\tBlock: duniform_like\r\n",
        "    \t\t\tBlock: exponweib\r\n",
        "    \t\t\tBlock: exponweib_gx\r\n",
        "    \t\t\tBlock: exponweib_gl\r\n",
        "    \t\t\tBlock: exponweib_gk\r\n",
        "    \t\t\tBlock: exponweib_ga\r\n",
        "    \t\t\tBlock: exponweib_gs\r\n",
        "    \t\t\tBlock: exponweib_ppf\r\n",
        "    \t\t\tBlock: constrain\r\n",
        "    \t\t\tBlock: poisson\r\n",
        "    \t\t\tBlock: poisson_gmu\r\n",
        "    \t\t\tBlock: trpoisson\r\n",
        "    \t\t\tBlock: trpoisson_gmu\r\n",
        "    \t\t\tBlock: t\r\n",
        "    \t\t\tBlock: t_grad_x\r\n",
        "    \t\t\tBlock: t_grad_nu\r\n",
        "    \t\t\tBlock: chi2_grad_nu\r\n",
        "    \t\t\tBlock: nct\r\n",
        "    \t\t\tBlock: multinomial\r\n",
        "    \t\t\tBlock: weibull\r\n",
        "    \t\t\tBlock: weibull_gx\r\n",
        "    \t\t\tBlock: weibull_ga\r\n",
        "    \t\t\tBlock: weibull_gb\r\n",
        "    \t\t\tBlock: logistic\r\n",
        "    \t\t\tBlock: normal\r\n",
        "    \t\t\tBlock: normal_grad_tau\r\n",
        "    \t\t\tBlock: normal_grad_x\r\n",
        "    \t\t\tBlock: normal_grad_mu\r\n",
        "    \t\t\tBlock: hnormal\r\n",
        "    \t\t\tBlock: hnormal_gradx\r\n",
        "    \t\t\tBlock: hnormal_gradtau\r\n",
        "    \t\t\tBlock: lognormal\r\n",
        "    \t\t\tBlock: lognormal_gradx\r\n",
        "    \t\t\tBlock: lognormal_gradmu\r\n",
        "    \t\t\tBlock: lognormal_gradtau\r\n",
        "    \t\t\tBlock: arlognormal\r\n",
        "    \t\t\tBlock: gev\r\n",
        "    \t\t\tBlock: gev_ppf\r\n",
        "    \t\t\tBlock: gamma\r\n",
        "    \t\t\tBlock: gamma_grad_x\r\n",
        "    \t\t\tBlock: gamma_grad_alpha\r\n",
        "    \t\t\tBlock: gamma_grad_beta\r\n",
        "    \t\t\tBlock: igamma\r\n",
        "    \t\t\tBlock: igamma_grad_x\r\n",
        "    \t\t\tBlock: igamma_grad_alpha\r\n",
        "    \t\t\tBlock: igamma_grad_beta\r\n",
        "    \t\t\tBlock: hyperg\r\n",
        "    \t\t\tBlock: geometric\r\n",
        "    \t\t\tBlock: geometric_gp\r\n",
        "    \t\t\tBlock: dirichlet\r\n",
        "    \t\t\tBlock: cauchy\r\n",
        "    \t\t\tBlock: cauchy_grad_x\r\n",
        "    \t\t\tBlock: cauchy_grad_a\r\n",
        "    \t\t\tBlock: cauchy_grad_b\r\n",
        "    \t\t\tBlock: negbin\r\n",
        "    \t\t\tBlock: negbin2\r\n",
        "    \t\t\tBlock: negbin2_gmu\r\n",
        "    \t\t\tBlock: negbin2_ga\r\n",
        "    \t\t\tBlock: binomial\r\n",
        "    \t\t\tBlock: binomial_gp\r\n",
        "    \t\t\tBlock: bernoulli\r\n",
        "    \t\t\tBlock: bern_grad_p\r\n",
        "    \t\t\tBlock: beta_like\r\n",
        "    \t\t\tBlock: beta_grad_x\r\n",
        "    \t\t\tBlock: beta_grad_a\r\n",
        "    \t\t\tBlock: beta_grad_b\r\n",
        "    \t\t\tBlock: betabin_like\r\n",
        "    \t\t\tBlock: betabin_ga\r\n",
        "    \t\t\tBlock: betabin_gb\r\n",
        "    \t\t\tBlock: mvhyperg\r\n",
        "    \t\t\tBlock: dirmultinom\r\n",
        "    \t\t\tBlock: wishart\r\n",
        "    \t\t\tBlock: trace\r\n",
        "    \t\t\tBlock: gamfun\r\n",
        "    \t\t\tBlock: gammds\r\n",
        "    \t\t\tBlock: psi\r\n",
        "    \t\t\tBlock: gser\r\n",
        "    \t\t\tBlock: gcf\r\n",
        "    \t\t\tBlock: gammq\r\n",
        "    \t\t\tBlock: trans\r\n",
        "    \t\t\tBlock: matmult\r\n",
        "    \t\t\tBlock: dtrm\r\n",
        "    \t\t\tBlock: elgs\r\n",
        "    \t\t\tBlock: bico\r\n",
        "    \t\t\tBlock: chol\r\n",
        "    \t\t\tBlock: hermpoly\r\n",
        "    \t\t\tBlock: set_uniform\r\n",
        "    \t\t\tBlock: categorical\r\n",
        "    \t\t\tBlock: rcat\r\n",
        "    \t\t\tBlock: logit\r\n",
        "    \t\t\tBlock: invlogit\r\n",
        "    \t\t\tBlock: stukel_logit\r\n",
        "    \t\t\tBlock: stukel_invlogit\r\n",
        "    \t\t\tBlock: vonmises\r\n",
        "    \t\t\tBlock: pareto\r\n",
        "    \t\t\tBlock: truncated_pareto\r\n",
        "    \t\t\tBlock: fixed_binsize\r\n",
        "    \t\t\tBlock: weighted_fixed_binsize\r\n",
        "    \t\t\tBlock: fixed_binsize_nd\r\n",
        "    {'attrspec': ['dimension(n)']}\r\n",
        "    In: :flib:pymc/histogram.f:qsorti\r\n",
        "    vars2fortran: No typespec for argument \"ord\".\r\n",
        "    \t\t\tBlock: qsorti\r\n",
        "    \t\t\tBlock: checksymm\r\n",
        "    \t\t\tBlock: chol_mvnorm\r\n",
        "    \t\t\tBlock: cov_mvnorm\r\n",
        "    \t\t\tBlock: prec_mvnorm\r\n",
        "    \t\t\tBlock: blas_wishart\r\n",
        "    \t\t\tBlock: blas_wishart_cov\r\n",
        "    {'attrspec': ['intent(hide)']}\r\n",
        "    In: :flib:pymc/blas_wrap.f:dcopy_wrap\r\n",
        "    vars2fortran: No typespec for argument \"nx\".\r\n",
        "    \t\t\tBlock: dcopy_wrap\r\n",
        "    \t\t\tBlock: dtrmm_wrap\r\n",
        "    \t\t\tBlock: dtrsm_wrap\r\n",
        "    \t\t\tBlock: dpotrf_wrap\r\n",
        "    \t\t\tBlock: dchdc_wrap\r\n",
        "    \t\t\tBlock: dpotrs_wrap\r\n",
        "    \t\t\tBlock: ppnd16\r\n",
        "    \t\t\tBlock: calerf\r\n",
        "    \t\t\tBlock: derf\r\n",
        "    \t\t\tBlock: derfc\r\n",
        "    \t\t\tBlock: derfcx\r\n",
        "    \t\t\tBlock: gibbsit\r\n",
        "    \t\t\tBlock: matinput\r\n",
        "    \t\t\tBlock: vecinput\r\n",
        "    \t\t\tBlock: oneparse\r\n",
        "    \t\t\tBlock: gibbmain\r\n",
        "    \t\t\tBlock: empquant\r\n",
        "    \t\t\tBlock: dichot\r\n",
        "    \t\t\tBlock: thin\r\n",
        "    \t\t\tBlock: mctest\r\n",
        "    \t\t\tBlock: indtest\r\n",
        "    \t\t\tBlock: mcest\r\n",
        "    \t\t\tBlock: ppnd7\r\n",
        "    \t\t\tBlock: ssort\r\n",
        "    Post-processing (stage 2)...\r\n",
        "    Building modules...\r\n",
        "    \tBuilding module \"flib\"...\r\n",
        "    \t\tConstructing wrapper function \"symmetrize\"...\r\n",
        "    \t\t  symmetrize(c,[cmin,cmax])\r\n",
        "    \t\tConstructing wrapper function \"logsum\"...\r\n",
        "    \t\t  s = logsum(x)\r\n",
        "    \t\tConstructing wrapper function \"logsum_cpx\"...\r\n",
        "    \t\t  s = logsum_cpx(x)\r\n",
        "    \t\tCreating wrapper for Fortran function \"combinationln\"(\"combinationln\")...\r\n",
        "    \t\tConstructing wrapper function \"combinationln\"...\r\n",
        "    \t\t  combinationln = combinationln(n,k)\r\n",
        "    \t\tConstructing wrapper function \"expand_triangular\"...\r\n",
        "    \t\t  t = expand_triangular(d,f)\r\n",
        "    \t\tConstructing wrapper function \"mod_to_circle\"...\r\n",
        "    \t\t  mx = mod_to_circle(x,u,l)\r\n",
        "    \t\tConstructing wrapper function \"standardize\"...\r\n",
        "    \t\t  z = standardize(x,loc,scale)\r\n",
        "    \t\tCreating wrapper for Fortran function \"gammln\"(\"gammln\")...\r\n",
        "    \t\tConstructing wrapper function \"gammln\"...\r\n",
        "    \t\t  gammln = gammln(xx)\r\n",
        "    \t\tCreating wrapper for Fortran function \"mvgammln\"(\"mvgammln\")...\r\n",
        "    \t\tConstructing wrapper function \"mvgammln\"...\r\n",
        "    \t\t  mvgammln = mvgammln(x,k)\r\n",
        "    \t\tCreating wrapper for Fortran function \"factrl\"(\"factrl\")...\r\n",
        "    \t\tConstructing wrapper function \"factrl\"...\r\n",
        "    \t\t  factrl = factrl(n)\r\n",
        "    \t\tCreating wrapper for Fortran function \"factln\"(\"factln\")...\r\n",
        "    \t\tConstructing wrapper function \"factln\"...\r\n",
        "    \t\t  factln = factln(n)\r\n",
        "    \t\tConstructing wrapper function \"normcdf\"...\r\n",
        "    \t\t  normcdf(x)\r\n",
        "    \t\tConstructing wrapper function \"sn_like\"...\r\n",
        "    \t\t  like = sn_like(x,mu,tau,alph)\r\n",
        "    \t\tConstructing wrapper function \"rskewnorm\"...\r\n",
        "    \t\t  x = rskewnorm(nx,mu,tau,alph,rn)\r\n",
        "    \t\tConstructing wrapper function \"uniform_like\"...\r\n",
        "    \t\t  like = uniform_like(x,lower,upper)\r\n",
        "    \t\tConstructing wrapper function \"uniform_grad_x\"...\r\n",
        "    \t\t  gradxlike = uniform_grad_x(x,lower,upper)\r\n",
        "    \t\tConstructing wrapper function \"uniform_grad_l\"...\r\n",
        "    \t\t  gradllike = uniform_grad_l(x,lower,upper)\r\n",
        "    \t\tConstructing wrapper function \"uniform_grad_u\"...\r\n",
        "    \t\t  gradulike = uniform_grad_u(x,lower,upper)\r\n",
        "    \t\tConstructing wrapper function \"duniform_like\"...\r\n",
        "    \t\t  like = duniform_like(x,lower,upper)\r\n",
        "    \t\tConstructing wrapper function \"exponweib\"...\r\n",
        "    \t\t  like = exponweib(x,a,c,loc,scale)\r\n",
        "    \t\tConstructing wrapper function \"exponweib_gx\"...\r\n",
        "    \t\t  gradlike = exponweib_gx(x,alpha,k,loc,scale)\r\n",
        "    \t\tConstructing wrapper function \"exponweib_gl\"...\r\n",
        "    \t\t  gradlike = exponweib_gl(x,alpha,k,loc,scale)\r\n",
        "    \t\tConstructing wrapper function \"exponweib_gk\"...\r\n",
        "    \t\t  gradlike = exponweib_gk(x,alpha,k,loc,scale)\r\n",
        "    \t\tConstructing wrapper function \"exponweib_ga\"...\r\n",
        "    \t\t  gradlike = exponweib_ga(x,alpha,k,loc,scale,[nk])\r\n",
        "    \t\tConstructing wrapper function \"exponweib_gs\"...\r\n",
        "    \t\t  gradlike = exponweib_gs(x,alpha,k,loc,scale)\r\n",
        "    \t\tConstructing wrapper function \"exponweib_ppf\"...\r\n",
        "    \t\t  ppf = exponweib_ppf(q,a,c)\r\n",
        "    \t\tConstructing wrapper function \"constrain\"...\r\n",
        "    \t\t  pass = constrain(x,a,b,allow_equal)\r\n",
        "    \t\tConstructing wrapper function \"poisson\"...\r\n",
        "    \t\t  like = poisson(x,mu)\r\n",
        "    \t\tConstructing wrapper function \"poisson_gmu\"...\r\n",
        "    \t\t  gradlike = poisson_gmu(x,mu)\r\n",
        "    \t\tConstructing wrapper function \"trpoisson\"...\r\n",
        "    \t\t  like = trpoisson(x,mu,k)\r\n",
        "    \t\tConstructing wrapper function \"trpoisson_gmu\"...\r\n",
        "    \t\t  gradlike = trpoisson_gmu(x,mu,k)\r\n",
        "    \t\tConstructing wrapper function \"t\"...\r\n",
        "    \t\t  like = t(x,nu)\r\n",
        "    \t\tConstructing wrapper function \"t_grad_x\"...\r\n",
        "    \t\t  gradlikex = t_grad_x(x,nu)\r\n",
        "    \t\tConstructing wrapper function \"t_grad_nu\"...\r\n",
        "    \t\t  gradlikenu = t_grad_nu(x,nu)\r\n",
        "    \t\tConstructing wrapper function \"chi2_grad_nu\"...\r\n",
        "    \t\t  gradlikenu = chi2_grad_nu(x,nu)\r\n",
        "    \t\tConstructing wrapper function \"nct\"...\r\n",
        "    \t\t  like = nct(x,mu,lam,nu)\r\n",
        "    \t\tConstructing wrapper function \"multinomial\"...\r\n",
        "    \t\t  like = multinomial(x,n,p)\r\n",
        "    \t\tConstructing wrapper function \"weibull\"...\r\n",
        "    \t\t  like = weibull(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"weibull_gx\"...\r\n",
        "    \t\t  gradlike = weibull_gx(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"weibull_ga\"...\r\n",
        "    \t\t  gradlike = weibull_ga(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"weibull_gb\"...\r\n",
        "    \t\t  gradlike = weibull_gb(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"logistic\"...\r\n",
        "    \t\t  like = logistic(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"normal\"...\r\n",
        "    \t\t  like = normal(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"normal_grad_tau\"...\r\n",
        "    \t\t  grad_tau_like = normal_grad_tau(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"normal_grad_x\"...\r\n",
        "    \t\t  grad_x_like = normal_grad_x(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"normal_grad_mu\"...\r\n",
        "    \t\t  gradmulike = normal_grad_mu(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"hnormal\"...\r\n",
        "    \t\t  like = hnormal(x,tau)\r\n",
        "    \t\tConstructing wrapper function \"hnormal_gradx\"...\r\n",
        "    \t\t  gradlike = hnormal_gradx(x,tau)\r\n",
        "    \t\tConstructing wrapper function \"hnormal_gradtau\"...\r\n",
        "    \t\t  gradlike = hnormal_gradtau(x,tau)\r\n",
        "    \t\tConstructing wrapper function \"lognormal\"...\r\n",
        "    \t\t  like = lognormal(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"lognormal_gradx\"...\r\n",
        "    \t\t  gradlike = lognormal_gradx(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"lognormal_gradmu\"...\r\n",
        "    \t\t  gradlike = lognormal_gradmu(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"lognormal_gradtau\"...\r\n",
        "    \t\t  gradlike = lognormal_gradtau(x,mu,tau)\r\n",
        "    \t\tConstructing wrapper function \"arlognormal\"...\r\n",
        "    \t\t  like = arlognormal(x,mu,sigma,rho,beta)\r\n",
        "    \t\tConstructing wrapper function \"gev\"...\r\n",
        "    \t\t  like = gev(x,xi,mu,sigma)\r\n",
        "    \t\tConstructing wrapper function \"gev_ppf\"...\r\n",
        "    \t\t  ppf = gev_ppf(q,xi)\r\n",
        "    \t\tConstructing wrapper function \"gamma\"...\r\n",
        "    \t\t  like = gamma(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"gamma_grad_x\"...\r\n",
        "    \t\t  gradxlike = gamma_grad_x(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"gamma_grad_alpha\"...\r\n",
        "    \t\t  gradalphalike = gamma_grad_alpha(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"gamma_grad_beta\"...\r\n",
        "    \t\t  gradbetalike = gamma_grad_beta(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"igamma\"...\r\n",
        "    \t\t  like = igamma(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"igamma_grad_x\"...\r\n",
        "    \t\t  gradxlike = igamma_grad_x(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"igamma_grad_alpha\"...\r\n",
        "    \t\t  gradalphalike = igamma_grad_alpha(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"igamma_grad_beta\"...\r\n",
        "    \t\t  gradbetalike = igamma_grad_beta(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"hyperg\"...\r\n",
        "    \t\t  like = hyperg(x,draws,success,total)\r\n",
        "    \t\tConstructing wrapper function \"geometric\"...\r\n",
        "    \t\t  like = geometric(x,p)\r\n",
        "    \t\tConstructing wrapper function \"geometric_gp\"...\r\n",
        "    \t\t  gradlike = geometric_gp(x,p)\r\n",
        "    \t\tConstructing wrapper function \"dirichlet\"...\r\n",
        "    \t\t  like = dirichlet(x,theta)\r\n",
        "    \t\tConstructing wrapper function \"cauchy\"...\r\n",
        "    \t\t  like = cauchy(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"cauchy_grad_x\"...\r\n",
        "    \t\t  gradlike = cauchy_grad_x(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"cauchy_grad_a\"...\r\n",
        "    \t\t  gradlike = cauchy_grad_a(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"cauchy_grad_b\"...\r\n",
        "    \t\t  gradlike = cauchy_grad_b(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"negbin\"...\r\n",
        "    \t\t  like = negbin(x,r,p)\r\n",
        "    \t\tConstructing wrapper function \"negbin2\"...\r\n",
        "    \t\t  like = negbin2(x,mu,a)\r\n",
        "    \t\tConstructing wrapper function \"negbin2_gmu\"...\r\n",
        "    \t\t  gradlike = negbin2_gmu(x,mu,alpha)\r\n",
        "    \t\tConstructing wrapper function \"negbin2_ga\"...\r\n",
        "    \t\t  gradlike = negbin2_ga(x,mu,alpha)\r\n",
        "    \t\tConstructing wrapper function \"binomial\"...\r\n",
        "    \t\t  like = binomial(x,n,p)\r\n",
        "    \t\tConstructing wrapper function \"binomial_gp\"...\r\n",
        "    \t\t  gradlike = binomial_gp(x,n,p)\r\n",
        "    \t\tConstructing wrapper function \"bernoulli\"...\r\n",
        "    \t\t  like = bernoulli(x,p)\r\n",
        "    \t\tConstructing wrapper function \"bern_grad_p\"...\r\n",
        "    \t\t  grad_like = bern_grad_p(x,p)\r\n",
        "    \t\tConstructing wrapper function \"beta_like\"...\r\n",
        "    \t\t  like = beta_like(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"beta_grad_x\"...\r\n",
        "    \t\t  gradlikex = beta_grad_x(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"beta_grad_a\"...\r\n",
        "    \t\t  gradlikea = beta_grad_a(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"beta_grad_b\"...\r\n",
        "    \t\t  gradlikeb = beta_grad_b(x,alpha,beta)\r\n",
        "    \t\tConstructing wrapper function \"betabin_like\"...\r\n",
        "    \t\t  like = betabin_like(x,alpha,beta,n)\r\n",
        "    \t\tConstructing wrapper function \"betabin_ga\"...\r\n",
        "    \t\t  gradlike = betabin_ga(x,alpha,beta,n)\r\n",
        "    \t\tConstructing wrapper function \"betabin_gb\"...\r\n",
        "    \t\t  gradlike = betabin_gb(x,alpha,beta,n)\r\n",
        "    \t\tConstructing wrapper function \"mvhyperg\"...\r\n",
        "    \t\t  like = mvhyperg(x,color)\r\n",
        "    \t\tConstructing wrapper function \"dirmultinom\"...\r\n",
        "    \t\t  like = dirmultinom(x,theta)\r\n",
        "    \t\tConstructing wrapper function \"wishart\"...\r\n",
        "    \t\t  like = wishart(x,n,sigma)\r\n",
        "    \t\tConstructing wrapper function \"trace\"...\r\n",
        "    \t\t  trace(mat,tr,[k])\r\n",
        "    \t\tConstructing wrapper function \"gamfun\"...\r\n",
        "    \t\t  gx = gamfun(xx)\r\n",
        "    \t\tCreating wrapper for Fortran function \"gammds\"(\"gammds\")...\r\n",
        "    \t\tConstructing wrapper function \"gammds\"...\r\n",
        "    \t\t  gammds = gammds(y,p,ifault)\r\n",
        "    \t\tCreating wrapper for Fortran function \"psi\"(\"psi\")...\r\n",
        "    \t\tConstructing wrapper function \"psi\"...\r\n",
        "    \t\t  psi = psi(x)\r\n",
        "    \t\tConstructing wrapper function \"gser\"...\r\n",
        "    \t\t  gser(gamser,a,x,gln)\r\n",
        "    \t\tConstructing wrapper function \"gcf\"...\r\n",
        "    \t\t  gcf(gammcf,a,x,gln)\r\n",
        "    \t\tCreating wrapper for Fortran function \"gammq\"(\"gammq\")...\r\n",
        "    \t\tConstructing wrapper function \"gammq\"...\r\n",
        "    \t\t  gammq = gammq(a,x)\r\n",
        "    \t\tConstructing wrapper function \"trans\"...\r\n",
        "    \t\t  tmat = trans(mat)\r\n",
        "    \t\tConstructing wrapper function \"matmult\"...\r\n",
        "    \t\t  prod = matmult(mat1,mat2)\r\n",
        "    \t\tConstructing wrapper function \"dtrm\"...\r\n",
        "    \t\t  d = dtrm(a)\r\n",
        "    \t\tConstructing wrapper function \"elgs\"...\r\n",
        "    \t\t  elgs(a,indx,[n])\r\n",
        "    \t\tCreating wrapper for Fortran function \"bico\"(\"bico\")...\r\n",
        "    \t\tConstructing wrapper function \"bico\"...\r\n",
        "    \t\t  bico = bico(n,k)\r\n",
        "    \t\tConstructing wrapper function \"chol\"...\r\n",
        "    \t\t  c = chol(a,[n])\r\n",
        "    \t\tConstructing wrapper function \"hermpoly\"...\r\n",
        "    \t\t  cx = hermpoly(n,x)\r\n",
        "    \t\tConstructing wrapper function \"set_uniform\"...\r\n",
        "    \t\t  set_uniform(seed1,seed2)\r\n",
        "    \t\tConstructing wrapper function \"categorical\"...\r\n",
        "    \t\t  like = categorical(x,p)\r\n",
        "    \t\tConstructing wrapper function \"rcat\"...\r\n",
        "    \t\t  s = rcat(p,rands)\r\n",
        "    \t\tConstructing wrapper function \"logit\"...\r\n",
        "    \t\t  ltheta = logit(theta)\r\n",
        "    \t\tConstructing wrapper function \"invlogit\"...\r\n",
        "    \t\t  theta = invlogit(ltheta)\r\n",
        "    \t\tConstructing wrapper function \"stukel_logit\"...\r\n",
        "    \t\t  ltheta = stukel_logit(theta,a1,a2,[overwrite_theta])\r\n",
        "    \t\tConstructing wrapper function \"stukel_invlogit\"...\r\n",
        "    \t\t  theta = stukel_invlogit(ltheta,a1,a2,[overwrite_ltheta])\r\n",
        "    \t\tConstructing wrapper function \"vonmises\"...\r\n",
        "    \t\t  like = vonmises(x,mu,kappa)\r\n",
        "    \t\tConstructing wrapper function \"pareto\"...\r\n",
        "    \t\t  like = pareto(x,alpha,m)\r\n",
        "    \t\tConstructing wrapper function \"truncated_pareto\"...\r\n",
        "    \t\t  like = truncated_pareto(x,alpha,m,b)\r\n",
        "    \t\tConstructing wrapper function \"fixed_binsize\"...\r\n",
        "    \t\t  h = fixed_binsize(x,bin0,delta,n)\r\n",
        "    \t\tConstructing wrapper function \"weighted_fixed_binsize\"...\r\n",
        "    \t\t  h = weighted_fixed_binsize(x,w,bin0,delta,n)\r\n",
        "    \t\tConstructing wrapper function \"fixed_binsize_nd\"...\r\n",
        "    \t\t  count = fixed_binsize_nd(x,bin0,delta,n,nc)\r\n",
        "    \t\tConstructing wrapper function \"qsorti\"...\r\n",
        "    \t\t  qsorti(ord,a,[n])\r\n",
        "    \t\tConstructing wrapper function \"checksymm\"...\r\n",
        "    \t\t  cs = checksymm(x)\r\n",
        "    \t\tConstructing wrapper function \"chol_mvnorm\"...\r\n",
        "    \t\t  like = chol_mvnorm(x,mu,sig,[overwrite_x,overwrite_mu])\r\n",
        "    \t\tConstructing wrapper function \"cov_mvnorm\"...\r\n",
        "    \t\t  like = cov_mvnorm(x,mu,c,[overwrite_x,overwrite_mu,overwrite_c])\r\n",
        "    \t\tConstructing wrapper function \"prec_mvnorm\"...\r\n",
        "    \t\t  like = prec_mvnorm(x,mu,tau,[overwrite_x,overwrite_mu,overwrite_tau])\r\n",
        "    \t\tConstructing wrapper function \"blas_wishart\"...\r\n",
        "    \t\t  like = blas_wishart(x,n,t,[overwrite_x,overwrite_t])\r\n",
        "    \t\tConstructing wrapper function \"blas_wishart_cov\"...\r\n",
        "    \t\t  like = blas_wishart_cov(x,n,v,[overwrite_x,overwrite_v])\r\n",
        "    \t\tConstructing wrapper function \"dcopy_wrap\"...\r\n",
        "    \t\t  dcopy_wrap(x,y)\r\n",
        "    \t\tConstructing wrapper function \"dtrmm_wrap\"...\r\n",
        "    \t\t  dtrmm_wrap(a,b,side,transa,uplo,alpha)\r\n",
        "    \t\tConstructing wrapper function \"dtrsm_wrap\"...\r\n",
        "    \t\t  dtrsm_wrap(a,b,side,transa,uplo,alpha)\r\n",
        "    \t\tConstructing wrapper function \"dpotrf_wrap\"...\r\n",
        "    \t\t  info = dpotrf_wrap(a)\r\n",
        "    \t\tConstructing wrapper function \"dchdc_wrap\"...\r\n",
        "    \t\t  piv,info = dchdc_wrap(a)\r\n",
        "    \t\tConstructing wrapper function \"dpotrs_wrap\"...\r\n",
        "    \t\t  info = dpotrs_wrap(chol_fac,b,[uplo])\r\n",
        "    \t\tCreating wrapper for Fortran function \"ppnd16\"(\"ppnd16\")...\r\n",
        "    \t\tConstructing wrapper function \"ppnd16\"...\r\n",
        "    \t\t  ppnd16 = ppnd16(p,ifault)\r\n",
        "    \t\tConstructing wrapper function \"calerf\"...\r\n",
        "    \t\t  calerf(arg,result,jint)\r\n",
        "    \t\tCreating wrapper for Fortran function \"derf\"(\"derf\")...\r\n",
        "    \t\tConstructing wrapper function \"derf\"...\r\n",
        "    \t\t  derf = derf(x)\r\n",
        "    \t\tCreating wrapper for Fortran function \"derfc\"(\"derfc\")...\r\n",
        "    \t\tConstructing wrapper function \"derfc\"...\r\n",
        "    \t\t  derfc = derfc(x)\r\n",
        "    \t\tCreating wrapper for Fortran function \"derfcx\"(\"derfcx\")...\r\n",
        "    \t\tConstructing wrapper function \"derfcx\"...\r\n",
        "    \t\t  derfcx = derfcx(x)\r\n",
        "    \t\tConstructing wrapper function \"matinput\"...\r\n",
        "    \t\t  matinput(uid,matout,rowused,colused,r15,[rowmax,colmax])\r\n",
        "    \t\tConstructing wrapper function \"vecinput\"...\r\n",
        "    \t\t  vecinput(uid,vecout,vecused,r15,[vecmax])\r\n",
        "    \t\tConstructing wrapper function \"oneparse\"...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    getarrdims:warning: assumed shape array, using 0 instead of '(*)'\r\n",
        "    \t\t  oneparse(instring,delimit,tokens,tokcnt,r15,[maxtok])\r\n",
        "    \t\tConstructing wrapper function \"gibbmain\"...\r\n",
        "    \t\t  nmin,kthin,nburn,nprec,kmind = gibbmain(original,q,r,s,epsilon)\r\n",
        "    \t\tCreating wrapper for Fortran function \"empquant\"(\"empquant\")...\r\n",
        "    \t\tConstructing wrapper function \"empquant\"...\r\n",
        "    getarrdims:warning: assumed shape array, using 0 instead of '*'\r\n",
        "    \t\t  empquant = empquant(data,q,work,[iteracnt])\r\n",
        "    \t\tConstructing wrapper function \"dichot\"...\r\n",
        "    \t\t  dichot(data,cutpt,zt,[iteracnt])\r\n",
        "    \t\tConstructing wrapper function \"thin\"...\r\n",
        "    \t\t  thin(series,kthin,result,thincnt,[iteracnt])\r\n",
        "    \t\tConstructing wrapper function \"mctest\"...\r\n",
        "    \t\t  mctest(data,g2,bic,[datacnt])\r\n",
        "    \t\tConstructing wrapper function \"indtest\"...\r\n",
        "    \t\t  indtest(data,g2,bic,[datacnt])\r\n",
        "    \t\tConstructing wrapper function \"mcest\"...\r\n",
        "    \t\t  mcest(data,alpha,beta,[datacnt])\r\n",
        "    \t\tCreating wrapper for Fortran function \"ppnd7\"(\"ppnd7\")...\r\n",
        "    \t\tConstructing wrapper function \"ppnd7\"...\r\n",
        "    \t\t  ppnd7 = ppnd7(p,ifault)\r\n",
        "    \t\tConstructing wrapper function \"ssort\"...\r\n",
        "    \t\t  ssort(x,y,kflag,[n])\r\n",
        "    \t\tConstructing COMMON block support for \"unif_seeds\"...\r\n",
        "    \t\t  s1,s2\r\n",
        "    \tWrote C/API module \"flib\" to file \"build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c\"\r\n",
        "    \tFortran 77 wrappers are saved to \"build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.f\"\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.f' to sources.\r\n",
        "    building extension \"pymc.LazyFunction\" sources\r\n",
        "    building extension \"pymc.Container_values\" sources\r\n",
        "    building extension \"pymc.gp.linalg_utils\" sources\r\n",
        "    f2py options: []\r\n",
        "    f2py:> build/src.macosx-10.5-x86_64-2.7/pymc/gp/linalg_utilsmodule.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    Reading fortran codes...\r\n",
        "    \tReading file 'pymc/gp/linalg_utils.f' (format:fix,strict)\r\n",
        "    \tReading file 'pymc/blas_wrap.f' (format:fix,strict)\r\n",
        "    Post-processing...\r\n",
        "    \tBlock: linalg_utils\r\n",
        "    \t\t\tBlock: remove_duplicates\r\n",
        "    \t\t\tBlock: check_repeats\r\n",
        "    \t\t\tBlock: diag_call\r\n",
        "    \t\t\t\t\tBlock: cov_fun\r\n",
        "    {'attrspec': ['intent(hide)']}\r\n",
        "    In: :linalg_utils:pymc/gp/linalg_utils.f:basis_diag_call\r\n",
        "    vars2fortran: No typespec for argument \"nbas\".\r\n",
        "    \t\t\tBlock: basis_diag_call\r\n",
        "    \t\t\tBlock: gp_array_logp\r\n",
        "    \t\t\tBlock: asqs\r\n",
        "    {'attrspec': ['intent(hide)']}\r\n",
        "    In: :linalg_utils:pymc/blas_wrap.f:dcopy_wrap\r\n",
        "    vars2fortran: No typespec for argument \"nx\".\r\n",
        "    \t\t\tBlock: dcopy_wrap\r\n",
        "    \t\t\tBlock: dtrmm_wrap\r\n",
        "    \t\t\tBlock: dtrsm_wrap\r\n",
        "    \t\t\tBlock: dpotrf_wrap\r\n",
        "    \t\t\tBlock: dchdc_wrap\r\n",
        "    \t\t\tBlock: dpotrs_wrap\r\n",
        "    Post-processing (stage 2)...\r\n",
        "    Building modules...\r\n",
        "    \tConstructing call-back function \"cb_cov_fun_in_diag_call__user__routines\"\r\n",
        "    \t  def cov_fun(xe): return q\r\n",
        "    \tBuilding module \"linalg_utils\"...\r\n",
        "    \t\tConstructing wrapper function \"remove_duplicates\"...\r\n",
        "    \t\t  nr,rf,rt,nu,xu,ui = remove_duplicates(x)\r\n",
        "    \t\tConstructing wrapper function \"check_repeats\"...\r\n",
        "    \t\t  f,new_indices,n_new_indices = check_repeats(x,x_sofar,f_sofar)\r\n",
        "    \t\tConstructing wrapper function \"diag_call\"...\r\n",
        "    \t\t  v = diag_call(x,cov_fun,[cov_fun_extra_args])\r\n",
        "    \t\tConstructing wrapper function \"basis_diag_call\"...\r\n",
        "    \t\t  v = basis_diag_call(basis_x)\r\n",
        "    \t\tConstructing wrapper function \"gp_array_logp\"...\r\n",
        "    \t\t  like = gp_array_logp(x,mu,sig,[overwrite_x,overwrite_mu])\r\n",
        "    \t\tConstructing wrapper function \"asqs\"...\r\n",
        "    \t\t  asqs(c,s,[cmin,cmax])\r\n",
        "    \t\tConstructing wrapper function \"dcopy_wrap\"...\r\n",
        "    \t\t  dcopy_wrap(x,y)\r\n",
        "    \t\tConstructing wrapper function \"dtrmm_wrap\"...\r\n",
        "    \t\t  dtrmm_wrap(a,b,side,transa,uplo,alpha)\r\n",
        "    \t\tConstructing wrapper function \"dtrsm_wrap\"...\r\n",
        "    \t\t  dtrsm_wrap(a,b,side,transa,uplo,alpha)\r\n",
        "    \t\tConstructing wrapper function \"dpotrf_wrap\"...\r\n",
        "    \t\t  info = dpotrf_wrap(a)\r\n",
        "    \t\tConstructing wrapper function \"dchdc_wrap\"...\r\n",
        "    \t\t  piv,info = dchdc_wrap(a)\r\n",
        "    \t\tConstructing wrapper function \"dpotrs_wrap\"...\r\n",
        "    \t\t  info = dpotrs_wrap(chol_fac,b,[uplo])\r\n",
        "    \tWrote C/API module \"linalg_utils\" to file \"build/src.macosx-10.5-x86_64-2.7/pymc/gp/linalg_utilsmodule.c\"\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "    building extension \"pymc.gp.incomplete_chol\" sources\r\n",
        "    f2py options: []\r\n",
        "    f2py:> build/src.macosx-10.5-x86_64-2.7/pymc/gp/incomplete_cholmodule.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    Reading fortran codes...\r\n",
        "    \tReading file 'pymc/gp/incomplete_chol.f' (format:fix,strict)\r\n",
        "    Post-processing...\r\n",
        "    \tBlock: incomplete_chol\r\n",
        "    \t\t\tBlock: ichol_continue\r\n",
        "    \t\t\t\t\tBlock: rowfun\r\n",
        "    \t\t\tBlock: ichol\r\n",
        "    \t\t\t\t\tBlock: rowfun\r\n",
        "    {'attrspec': ['intent(hide)']}\r\n",
        "    In: :incomplete_chol:pymc/gp/incomplete_chol.f:ichol_basis\r\n",
        "    vars2fortran: No typespec for argument \"n_nug\".\r\n",
        "    \t\t\tBlock: ichol_basis\r\n",
        "    \t\t\tBlock: ichol_full\r\n",
        "    Post-processing (stage 2)...\r\n",
        "    Building modules...\r\n",
        "    \tConstructing call-back function \"cb_rowfun_in_ichol_continue__user__routines\"\r\n",
        "    \t  def rowfun(itot,x,rowvec): return\r\n",
        "    \tConstructing call-back function \"cb_rowfun_in_ichol__user__routines\"\r\n",
        "    \t  def rowfun(i,x,rowvec): return\r\n",
        "    \tBuilding module \"incomplete_chol\"...\r\n",
        "    \t\tConstructing wrapper function \"ichol_continue\"...\r\n",
        "    \t\t  m,piv = ichol_continue(sig,diag,piv,reltol,x,rowfun,mold,[overwrite_x,rowfun_extra_args])\r\n",
        "    \t\tConstructing wrapper function \"ichol\"...\r\n",
        "    \t\t  sig,m,piv = ichol(diag,reltol,x,rowfun,rl,[overwrite_x,rowfun_extra_args])\r\n",
        "    \t\tConstructing wrapper function \"ichol_basis\"...\r\n",
        "    \t\t  sig,p,m = ichol_basis(basis,nug,reltol)\r\n",
        "    \t\tConstructing wrapper function \"ichol_full\"...\r\n",
        "    \t\t  sig,m,p = ichol_full(c,reltol)\r\n",
        "    \tWrote C/API module \"incomplete_chol\" to file \"build/src.macosx-10.5-x86_64-2.7/pymc/gp/incomplete_cholmodule.c\"\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "    building extension \"pymc.gp.cov_funs.isotropic_cov_funs\" sources\r\n",
        "    f2py options: []\r\n",
        "    f2py:> build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/isotropic_cov_funsmodule.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    Reading fortran codes...\r\n",
        "    \tReading file 'pymc/gp/cov_funs/isotropic_cov_funs.f' (format:fix,strict)\r\n",
        "    \tReading file 'blas/BLAS/dscal.f' (format:fix,strict)\r\n",
        "    Post-processing...\r\n",
        "    \tBlock: isotropic_cov_funs\r\n",
        "    \t\t\tBlock: imul\r\n",
        "    \t\t\tBlock: symmetrize\r\n",
        "    \t\t\tBlock: matern\r\n",
        "    \t\t\tBlock: nsmatrn\r\n",
        "    \t\t\tBlock: stein_spatiotemporal\r\n",
        "    \t\t\tBlock: nsst\r\n",
        "    \t\t\tBlock: gaussian\r\n",
        "    \t\t\tBlock: exponential\r\n",
        "    \t\t\tBlock: brownian\r\n",
        "    \t\t\tBlock: frac_brownian\r\n",
        "    \t\t\tBlock: pow_exp\r\n",
        "    \t\t\tBlock: sphere\r\n",
        "    \t\t\tBlock: quadratic\r\n",
        "    \t\t\tBlock: dgamma\r\n",
        "    \t\t\tBlock: rkbesl\r\n",
        "    \t\t\tBlock: dscal\r\n",
        "    Post-processing (stage 2)...\r\n",
        "    Building modules...\r\n",
        "    \tBuilding module \"isotropic_cov_funs\"...\r\n",
        "    \t\tConstructing wrapper function \"imul\"...\r\n",
        "    \t\t  imul(c,a,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"symmetrize\"...\r\n",
        "    \t\t  symmetrize(c,[cmin,cmax])\r\n",
        "    \t\tConstructing wrapper function \"matern\"...\r\n",
        "    \t\t  matern(c,diff_degree,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"nsmatrn\"...\r\n",
        "    \t\t  nsmatrn(c,ddx,ddy,hx,hy,nmax,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"stein_spatiotemporal\"...\r\n",
        "    \t\t  stein_spatiotemporal(c,gt,origin_val,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"nsst\"...\r\n",
        "    \t\t  nsst(c,gt,origin_val,hx,hy,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"gaussian\"...\r\n",
        "    \t\t  gaussian(c,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"exponential\"...\r\n",
        "    \t\t  exponential(c,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"brownian\"...\r\n",
        "    \t\t  brownian(c,x,y,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"frac_brownian\"...\r\n",
        "    \t\t  frac_brownian(c,x,y,h,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"pow_exp\"...\r\n",
        "    \t\t  pow_exp(c,pow,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"sphere\"...\r\n",
        "    \t\t  sphere(c,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"quadratic\"...\r\n",
        "    \t\t  quadratic(c,phi,[cmin,cmax,symm])\r\n",
        "    \t\tCreating wrapper for Fortran function \"dgamma\"(\"dgamma\")...\r\n",
        "    \t\tConstructing wrapper function \"dgamma\"...\r\n",
        "    \t\t  dgamma = dgamma(x)\r\n",
        "    \t\tConstructing wrapper function \"rkbesl\"...\r\n",
        "    \t\t  bk = rkbesl(x,alpha,nb,ize,ncalc)\r\n",
        "    \t\tConstructing wrapper function \"dscal\"...\r\n",
        "    getarrdims:warning: assumed shape array, using 0 instead of '*'\r\n",
        "    \t\t  dscal(n,da,dx,incx)\r\n",
        "    \tWrote C/API module \"isotropic_cov_funs\" to file \"build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/isotropic_cov_funsmodule.c\"\r\n",
        "    \tFortran 77 wrappers are saved to \"build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/isotropic_cov_funs-f2pywrappers.f\"\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/isotropic_cov_funs-f2pywrappers.f' to sources.\r\n",
        "    building extension \"pymc.gp.cov_funs.distances\" sources\r\n",
        "    f2py options: []\r\n",
        "    f2py:> build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/distancesmodule.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    Reading fortran codes...\r\n",
        "    \tReading file 'pymc/gp/cov_funs/distances.f' (format:fix,strict)\r\n",
        "    Post-processing...\r\n",
        "    \tBlock: distances\r\n",
        "    \t\t\tBlock: euclidean\r\n",
        "    \t\t\tBlock: geographic\r\n",
        "    \t\t\tBlock: paniso_geo_rad\r\n",
        "    \t\t\tBlock: aniso_geo_rad\r\n",
        "    Post-processing (stage 2)...\r\n",
        "    Building modules...\r\n",
        "    \tBuilding module \"distances\"...\r\n",
        "    \t\tConstructing wrapper function \"euclidean\"...\r\n",
        "    \t\t  euclidean(d,x,y,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"geographic\"...\r\n",
        "    \t\t  geographic(d,x,y,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"paniso_geo_rad\"...\r\n",
        "    \t\t  paniso_geo_rad(d,x,y,ctrs,scals,[cmin,cmax,symm])\r\n",
        "    \t\tConstructing wrapper function \"aniso_geo_rad\"...\r\n",
        "    \t\t  aniso_geo_rad(d,x,y,inc,ecc,[cmin,cmax,symm])\r\n",
        "    \tWrote C/API module \"distances\" to file \"build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/distancesmodule.c\"\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "    build_src: building npy-pkg config files\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    \r\n",
        "    warning: no files found matching 'README.txt'\r\n",
        "    warning: no files found matching 'INSTALL.txt'\r\n",
        "    warning: no files found matching 'docs/UserGuide.pdf'\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Installing collected packages: pymc\r\n",
        "  Running setup.py install for pymc\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    unifing config_cc, config, build_clib, build_ext, build commands --compiler options\r\n",
        "    unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\r\n",
        "    build_src\r\n",
        "    building extension \"pymc.flib\" sources\r\n",
        "    f2py options: ['skip:ppnd7']\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.f' to sources.\r\n",
        "    building extension \"pymc.LazyFunction\" sources\r\n",
        "    building extension \"pymc.Container_values\" sources\r\n",
        "    building extension \"pymc.gp.linalg_utils\" sources\r\n",
        "    f2py options: []\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "    building extension \"pymc.gp.incomplete_chol\" sources\r\n",
        "    f2py options: []\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "    building extension \"pymc.gp.cov_funs.isotropic_cov_funs\" sources\r\n",
        "    f2py options: []\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/isotropic_cov_funs-f2pywrappers.f' to sources.\r\n",
        "    building extension \"pymc.gp.cov_funs.distances\" sources\r\n",
        "    f2py options: []\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "      adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "    build_src: building npy-pkg config files\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    customize UnixCCompiler\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    customize UnixCCompiler using build_ext\r\n",
        "    customize Gnu95FCompiler\r\n",
        "    Found executable /usr/local/bin/gfortran\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    customize Gnu95FCompiler\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    customize Gnu95FCompiler using build_ext\r\n",
        "    building 'pymc.flib' extension\r\n",
        "    compiling C sources\r\n",
        "    C compiler: gcc -fno-strict-aliasing -I//anaconda/include -arch x86_64 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/usr/local/opt/zlib/include\r\n",
        "    \r\n",
        "    compile options: '-DNO_ATLAS_INFO=3 -Ibuild/src.macosx-10.5-x86_64-2.7 -I//anaconda/lib/python2.7/site-packages/numpy/core/include -I//anaconda/include/python2.7 -c'\r\n",
        "    extra options: '-msse3'\r\n",
        "    gcc: cephes/i0.c\r\n",
        "    gcc: cephes/c2f.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gcc: build/src.macosx-10.5-x86_64-2.7/fortranobject.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    In file included from build/src.macosx-10.5-x86_64-2.7/fortranobject.c:2:\r\n",
        "    In file included from build/src.macosx-10.5-x86_64-2.7/fortranobject.h:13:\r\n",
        "    In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15:\r\n",
        "    In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17:\r\n",
        "    In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728:\r\n",
        "    //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\r\n",
        "    #warning \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\"\r\n",
        "     ^\r\n",
        "    build/src.macosx-10.5-x86_64-2.7/fortranobject.c:338:30: warning: equality comparison with extraneous parentheses [-Wparentheses-equality]\r\n",
        "            if ((fp->defs[i].func==NULL)) {\r\n",
        "                 ~~~~~~~~~~~~~~~~^~~~~~\r\n",
        "    build/src.macosx-10.5-x86_64-2.7/fortranobject.c:338:30: note: remove extraneous parentheses around the comparison to silence this warning\r\n",
        "            if ((fp->defs[i].func==NULL)) {\r\n",
        "                ~                ^     ~\r\n",
        "    build/src.macosx-10.5-x86_64-2.7/fortranobject.c:338:30: note: use '=' to turn this equality comparison into an assignment\r\n",
        "            if ((fp->defs[i].func==NULL)) {\r\n",
        "                                 ^~\r\n",
        "                                 =\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    2 warnings generated.\r\n",
        "    gcc: build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    In file included from build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:18:\r\n",
        "    In file included from build/src.macosx-10.5-x86_64-2.7/fortranobject.h:13:\r\n",
        "    In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15:\r\n",
        "    In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17:\r\n",
        "    In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728:\r\n",
        "    //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\r\n",
        "    #warning \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\"\r\n",
        "     ^\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:16934:7: warning: array index 2 is past the end of the array (which contains 2 elements) [-Warray-bounds]\r\n",
        "      d = shape(x,2);\r\n",
        "          ^       ~\r\n",
        "    build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:86:24: note: expanded from macro 'shape'\r\n",
        "    #define shape(var,dim) var ## _Dims[dim]\r\n",
        "                           ^\r\n",
        "    <scratch space>:394:1: note: expanded from here\r\n",
        "    x_Dims\r\n",
        "    ^\r\n",
        "    build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:16835:3: note: array 'x_Dims' declared here\r\n",
        "      npy_intp x_Dims[2] = {-1, -1};\r\n",
        "      ^\r\n",
        "    build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:181:12: warning: unused function 'f2py_size' [-Wunused-function]\r\n",
        "    static int f2py_size(PyArrayObject* var, ...)\r\n",
        "               ^\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    3 warnings generated.\r\n",
        "    gcc: cephes/chbevl.c\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    compiling Fortran sources\r\n",
        "    Fortran f77 compiler: /usr/local/bin/gfortran -Wall -ffixed-form -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\r\n",
        "    Fortran f90 compiler: /usr/local/bin/gfortran -Wall -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\r\n",
        "    Fortran fix compiler: /usr/local/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\r\n",
        "    compile options: '-DNO_ATLAS_INFO=3 -Ibuild/src.macosx-10.5-x86_64-2.7 -I//anaconda/lib/python2.7/site-packages/numpy/core/include -I//anaconda/include/python2.7 -c'\r\n",
        "    gfortran:f77: pymc/flib.f\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "    Warning: Nonconforming tab character in column 1 of line 3661\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3662\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3664\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3665\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3666\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3716\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3717\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3718\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3720\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3721\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3722\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3724\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3725\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3726\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3732\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3846\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3847\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3849\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3851\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3875\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3876\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3877\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 3878\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4276\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4277\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4278\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4280\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4281\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4282\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4284\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4285\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4286\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4288\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4289\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4290\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4342\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4343\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4344\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4346\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4347\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4348\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4350\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4351\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4352\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4354\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4355\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4356\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4620\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4622\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4623\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4624\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4626\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4628\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4630\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4632\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4633\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4634\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4635\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4637\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4638\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4639\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4640\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4642\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4643\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4644\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4645\r\n",
        "    Warning: Nonconforming tab character in column 1 of line 4646\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    pymc/flib.f:316.55:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION mu_now, tau_now, alph_now, d_now, like\r\n",
        "                                                           1\r\n",
        "    Warning: Unused variable 'd_now' declared at (1)\r\n",
        "    pymc/flib.f:314.42:\r\n",
        "    \r\n",
        "          INTEGER i, nx, nalph, nmu, ntau, tnx\r\n",
        "                                              1\r\n",
        "    Warning: Unused variable 'tnx' declared at (1)\r\n",
        "    pymc/flib.f:442.71:\r\n",
        "    \r\n",
        "          subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "                                                                           1\r\n",
        "    Warning: Unused dummy argument 'gradxlike' at (1)\r\n",
        "    pymc/flib.f:461.40:\r\n",
        "    \r\n",
        "            DOUBLE PRECISION like, low, high\r\n",
        "                                            1\r\n",
        "    Warning: Unused variable 'high' declared at (1)\r\n",
        "    pymc/flib.f:458.36:\r\n",
        "    \r\n",
        "            INTEGER n, nlower, nupper, i\r\n",
        "                                        1\r\n",
        "    Warning: Unused variable 'i' declared at (1)\r\n",
        "    pymc/flib.f:461.29:\r\n",
        "    \r\n",
        "            DOUBLE PRECISION like, low, high\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'like' declared at (1)\r\n",
        "    pymc/flib.f:461.34:\r\n",
        "    \r\n",
        "            DOUBLE PRECISION like, low, high\r\n",
        "                                      1\r\n",
        "    Warning: Unused variable 'low' declared at (1)\r\n",
        "    pymc/flib.f:442.39:\r\n",
        "    \r\n",
        "          subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "                                           1\r\n",
        "    Warning: Unused dummy argument 'lower' at (1)\r\n",
        "    pymc/flib.f:442.45:\r\n",
        "    \r\n",
        "          subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "                                                 1\r\n",
        "    Warning: Unused dummy argument 'upper' at (1)\r\n",
        "    pymc/flib.f:442.33:\r\n",
        "    \r\n",
        "          subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "                                     1\r\n",
        "    Warning: Unused dummy argument 'x' at (1)\r\n",
        "    pymc/flib.f:692.41:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "                                             1\r\n",
        "    Warning: Unused variable 'pdf' declared at (1)\r\n",
        "    pymc/flib.f:760.41:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "                                             1\r\n",
        "    Warning: Unused variable 'pdf' declared at (1)\r\n",
        "    pymc/flib.f:834.41:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "                                             1\r\n",
        "    Warning: Unused variable 'pdf' declared at (1)\r\n",
        "    pymc/flib.f:906.41:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "                                             1\r\n",
        "    Warning: Unused variable 'pdf' declared at (1)\r\n",
        "    pymc/flib.f:976.41:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "                                             1\r\n",
        "    Warning: Unused variable 'pdf' declared at (1)\r\n",
        "    pymc/flib.f:1186.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION factln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'factln' declared at (1)\r\n",
        "    pymc/flib.f:1185.51:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION sumx, mut, infinity, sumfact\r\n",
        "                                                       1\r\n",
        "    Warning: Unused variable 'sumfact' declared at (1)\r\n",
        "    pymc/flib.f:1185.27:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION sumx, mut, infinity, sumfact\r\n",
        "                               1\r\n",
        "    Warning: Unused variable 'sumx' declared at (1)\r\n",
        "    pymc/flib.f:1300.55:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION mu(nmu), gradlike(nmu),grad, cdf\r\n",
        "                                                           1\r\n",
        "    Warning: Unused variable 'cdf' declared at (1)\r\n",
        "    pymc/flib.f:1302.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION factln, gammq\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'factln' declared at (1)\r\n",
        "    pymc/flib.f:1302.36:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION factln, gammq\r\n",
        "                                        1\r\n",
        "    Warning: Unused variable 'gammq' declared at (1)\r\n",
        "    pymc/flib.f:1301.59:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION sumx, mut, infinity, sumfact, sumcdf\r\n",
        "                                                               1\r\n",
        "    Warning: Unused variable 'sumcdf' declared at (1)\r\n",
        "    pymc/flib.f:1301.51:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION sumx, mut, infinity, sumfact, sumcdf\r\n",
        "                                                       1\r\n",
        "    Warning: Unused variable 'sumfact' declared at (1)\r\n",
        "    pymc/flib.f:1301.27:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION sumx, mut, infinity, sumfact, sumcdf\r\n",
        "                               1\r\n",
        "    Warning: Unused variable 'sumx' declared at (1)\r\n",
        "    pymc/flib.f:1402.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:2154.40:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gradlike(n), grad\r\n",
        "                                            1\r\n",
        "    Warning: Unused variable 'grad' declared at (1)\r\n",
        "    pymc/flib.f:2665.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:2728.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, psi\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:2793.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:2903.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:2959.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, psi\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:3021.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:3396.58:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gradlike(nx), atmp, btmp, PI, glike\r\n",
        "                                                              1\r\n",
        "    Warning: Unused variable 'glike' declared at (1)\r\n",
        "    pymc/flib.f:3646.37:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, factln\r\n",
        "                                         1\r\n",
        "    Warning: Unused variable 'factln' declared at (1)\r\n",
        "    pymc/flib.f:3646.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, factln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:3706.37:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, factln, psi\r\n",
        "                                         1\r\n",
        "    Warning: Unused variable 'factln' declared at (1)\r\n",
        "    pymc/flib.f:3706.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, factln, psi\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:3836.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION factln\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'factln' declared at (1)\r\n",
        "    pymc/flib.f:4039.26:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION psi\r\n",
        "                              1\r\n",
        "    Warning: Unused variable 'psi' declared at (1)\r\n",
        "    pymc/flib.f:4266.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, psi\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:4332.29:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION gammln, psi\r\n",
        "                                 1\r\n",
        "    Warning: Unused variable 'gammln' declared at (1)\r\n",
        "    pymc/flib.f:5184.35:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION like, factln, infinity, sump\r\n",
        "                                       1\r\n",
        "    Warning: Unused variable 'factln' declared at (1)\r\n",
        "    pymc/flib.f:5186.23:\r\n",
        "    \r\n",
        "          INTEGER i,j,n_tmp\r\n",
        "                           1\r\n",
        "    Warning: Unused variable 'n_tmp' declared at (1)\r\n",
        "    pymc/flib.f:5434.26:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION tmp\r\n",
        "                              1\r\n",
        "    Warning: Unused variable 'tmp' declared at (1)\r\n",
        "    pymc/flib.f:5488.25:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION i0\r\n",
        "                             1\r\n",
        "    Warning: Unused variable 'i0' declared at (1)\r\n",
        "    pymc/flib.f:5481.26:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION tmp\r\n",
        "                              1\r\n",
        "    Warning: Unused variable 'tmp' declared at (1)\r\n",
        "    pymc/flib.f:5535.25:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION i0\r\n",
        "                             1\r\n",
        "    Warning: Unused variable 'i0' declared at (1)\r\n",
        "    pymc/flib.f:5528.26:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION tmp\r\n",
        "                              1\r\n",
        "    Warning: Unused variable 'tmp' declared at (1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    pymc/flib.f: In function 'elgs':\r\n",
        "    pymc/flib.f:4882:0: warning: 'k' may be used uninitialized in this function [-Wmaybe-uninitialized]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    pymc/flib.f: In function 'exponweib_gl':\r\n",
        "    pymc/flib.f:778:0: warning: 'nc' may be used uninitialized in this function [-Wuninitialized]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    pymc/flib.f: In function 'exponweib_gs':\r\n",
        "    pymc/flib.f:994:0: warning: 'nc' may be used uninitialized in this function [-Wuninitialized]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gfortran:f77: pymc/histogram.f\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gfortran:f77: pymc/flib_blas.f\r\n",
        "    pymc/flib_blas.f:202.25:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION PI\r\n",
        "                             1\r\n",
        "    Warning: Unused variable 'pi' declared at (1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gfortran:f77: pymc/blas_wrap.f\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gfortran:f77: pymc/math.f\r\n",
        "    pymc/math.f:396.6:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION FUNCTION DERF(X)\r\n",
        "          1\r\n",
        "    Warning: 'derf' declared at (1) is also the name of an intrinsic.  It can only be called via an explicit interface or if declared EXTERNAL.\r\n",
        "    pymc/math.f:417.6:\r\n",
        "    \r\n",
        "          DOUBLE PRECISION FUNCTION DERFC(X)\r\n",
        "          1\r\n",
        "    Warning: 'derfc' declared at (1) is also the name of an intrinsic.  It can only be called via an explicit interface or if declared EXTERNAL.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gfortran:f77: pymc/gibbsit.f\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    pymc/gibbsit.f:2160.5:\r\n",
        "    \r\n",
        "      215 IF (R .GT. .5898437) GO TO 220\r\n",
        "         1\r\n",
        "    Warning: Label 215 at (1) defined but not used\r\n",
        "    pymc/gibbsit.f:2077.5:\r\n",
        "    \r\n",
        "      115 IF (R .GT. .5898437) GO TO 120\r\n",
        "         1\r\n",
        "    Warning: Label 115 at (1) defined but not used\r\n",
        "    pymc/gibbsit.f:2064.5:\r\n",
        "    \r\n",
        "       15 IF (KFLAG.GE.1) GO TO 30\r\n",
        "         1\r\n",
        "    Warning: Label 15 at (1) defined but not used\r\n",
        "    pymc/gibbsit.f:1204.48:\r\n",
        "    \r\n",
        "            cutpt = empquant(original,iteracnt,qhat,work)\r\n",
        "                                                    1\r\n",
        "    Warning: Type mismatch in argument 'work' at (1); passed INTEGER(4) to REAL(8)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    gfortran:f77: build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.f\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    /usr/local/bin/gfortran -Wall -L/usr/local/opt/zlib/lib build/temp.macosx-10.5-x86_64-2.7/cephes/i0.o build/temp.macosx-10.5-x86_64-2.7/cephes/c2f.o build/temp.macosx-10.5-x86_64-2.7/cephes/chbevl.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/fortranobject.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib.o build/temp.macosx-10.5-x86_64-2.7/pymc/histogram.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib_blas.o build/temp.macosx-10.5-x86_64-2.7/pymc/blas_wrap.o build/temp.macosx-10.5-x86_64-2.7/pymc/math.o build/temp.macosx-10.5-x86_64-2.7/pymc/gibbsit.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.o -L/usr/local/Cellar/gfortran/4.7.2/gfortran/lib/gcc/x86_64-apple-darwin12.2.0/4.7.2 -lgfortran -o build/lib.macosx-10.5-x86_64-2.7/pymc/flib.so -Wl,-framework -Wl,Accelerate\r\n",
        "    Undefined symbols for architecture x86_64:\r\n",
        "      \"_PyArg_ParseTupleAndKeywords\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyCObject_AsVoidPtr\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyCapsule_AsVoidPtr in fortranobject.o\r\n",
        "      \"_PyCObject_FromVoidPtr\", referenced from:\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "          _F2PyCapsule_FromVoidPtr in fortranobject.o\r\n",
        "      \"_PyCObject_Type\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyCapsule_Check in fortranobject.o\r\n",
        "      \"_PyComplex_FromDoubles\", referenced from:\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "      \"_PyComplex_Type\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyDict_DelItemString\", referenced from:\r\n",
        "          _fortran_setattr in fortranobject.o\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      \"_PyDict_GetItemString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "      \"_PyDict_New\", referenced from:\r\n",
        "          _PyFortranObject_New in fortranobject.o\r\n",
        "          _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyDict_SetItemString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyDict_SetItemString in fortranobject.o\r\n",
        "          _PyFortranObject_New in fortranobject.o\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyErr_Clear\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "          _F2PyDict_SetItemString in fortranobject.o\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyErr_Format\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_call in fortranobject.o\r\n",
        "      \"_PyErr_NewException\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyErr_Occurred\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyErr_Print\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyDict_SetItemString in fortranobject.o\r\n",
        "      \"_PyErr_SetString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyEval_RestoreThread\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "          _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyEval_SaveThread\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "          _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyExc_AttributeError\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyExc_ImportError\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyExc_MemoryError\", referenced from:\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "      \"_PyExc_RuntimeError\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_call in fortranobject.o\r\n",
        "      \"_PyExc_TypeError\", referenced from:\r\n",
        "          _fortran_call in fortranobject.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyExc_ValueError\", referenced from:\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyFloat_Type\", referenced from:\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyImport_ImportModule\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyMem_Free\", referenced from:\r\n",
        "          _fortran_dealloc in fortranobject.o\r\n",
        "      \"_PyModule_GetDict\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyNumber_Float\", referenced from:\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyNumber_Int\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "      \"_PyObject_GetAttrString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyObject_IsTrue\", referenced from:\r\n",
        "          _f2py_rout_flib_constrain in flibmodule.o\r\n",
        "      \"_PyObject_SetAttrString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyObject_Str\", referenced from:\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyObject_Type\", referenced from:\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PySequence_Check\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PySequence_GetItem\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyString_AsString\", referenced from:\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyString_ConcatAndDel\", referenced from:\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "      \"_PyString_FromFormat\", referenced from:\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyString_FromString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyType_IsSubtype\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyType_Type\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_Py_BuildValue\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_Py_FindMethod\", referenced from:\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "      \"_Py_InitModule4_64\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"__PyObject_New\", referenced from:\r\n",
        "          _PyFortranObject_New in fortranobject.o\r\n",
        "          _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "      \"__Py_NoneStruct\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "    ld: symbol(s) not found for architecture x86_64\r\n",
        "    collect2: error: ld returned 1 exit status\r\n",
        "    Undefined symbols for architecture x86_64:\r\n",
        "      \"_PyArg_ParseTupleAndKeywords\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyCObject_AsVoidPtr\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyCapsule_AsVoidPtr in fortranobject.o\r\n",
        "      \"_PyCObject_FromVoidPtr\", referenced from:\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "          _F2PyCapsule_FromVoidPtr in fortranobject.o\r\n",
        "      \"_PyCObject_Type\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyCapsule_Check in fortranobject.o\r\n",
        "      \"_PyComplex_FromDoubles\", referenced from:\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "      \"_PyComplex_Type\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyDict_DelItemString\", referenced from:\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyDict_GetItemString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "      \"_PyDict_New\", referenced from:\r\n",
        "          _PyFortranObject_New in fortranobject.o\r\n",
        "          _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyDict_SetItemString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyDict_SetItemString in fortranobject.o\r\n",
        "          _PyFortranObject_New in fortranobject.o\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyErr_Clear\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "          _F2PyDict_SetItemString in fortranobject.o\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyErr_Format\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_call in fortranobject.o\r\n",
        "      \"_PyErr_NewException\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyErr_Occurred\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyErr_Print\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _F2PyDict_SetItemString in fortranobject.o\r\n",
        "      \"_PyErr_SetString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyEval_RestoreThread\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "          _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyEval_SaveThread\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "          _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_PyExc_AttributeError\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_setattr in fortranobject.o\r\n",
        "      \"_PyExc_ImportError\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyExc_MemoryError\", referenced from:\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "      \"_PyExc_RuntimeError\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_call in fortranobject.o\r\n",
        "      \"_PyExc_TypeError\", referenced from:\r\n",
        "          _fortran_call in fortranobject.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyExc_ValueError\", referenced from:\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyFloat_Type\", referenced from:\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyImport_ImportModule\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyMem_Free\", referenced from:\r\n",
        "          _fortran_dealloc in fortranobject.o\r\n",
        "      \"_PyModule_GetDict\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyNumber_Float\", referenced from:\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyNumber_Int\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "      \"_PyObject_GetAttrString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyObject_IsTrue\", referenced from:\r\n",
        "          _f2py_rout_flib_constrain in flibmodule.o\r\n",
        "      \"_PyObject_SetAttrString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_PyObject_Str\", referenced from:\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyObject_Type\", referenced from:\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PySequence_Check\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PySequence_GetItem\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "      \"_PyString_AsString\", referenced from:\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyString_ConcatAndDel\", referenced from:\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "      \"_PyString_FromFormat\", referenced from:\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyString_FromString\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "          _fortran_repr in fortranobject.o\r\n",
        "      \"_PyType_IsSubtype\", referenced from:\r\n",
        "          _int_from_pyobj in flibmodule.o\r\n",
        "          _double_from_pyobj in flibmodule.o\r\n",
        "          _string_from_pyobj in flibmodule.o\r\n",
        "          _array_from_pyobj in fortranobject.o\r\n",
        "      \"_PyType_Type\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"_Py_BuildValue\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "      \"_Py_FindMethod\", referenced from:\r\n",
        "          _fortran_getattr in fortranobject.o\r\n",
        "      \"_Py_InitModule4_64\", referenced from:\r\n",
        "          _initflib in flibmodule.o\r\n",
        "      \"__PyObject_New\", referenced from:\r\n",
        "          _PyFortranObject_New in fortranobject.o\r\n",
        "          _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "      \"__Py_NoneStruct\", referenced from:\r\n",
        "          _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "          _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "          _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "          _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "          _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "          _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "          ...\r\n",
        "    ld: symbol(s) not found for architecture x86_64\r\n",
        "    collect2: error: ld returned 1 exit status\r\n",
        "    error: Command \"/usr/local/bin/gfortran -Wall -L/usr/local/opt/zlib/lib build/temp.macosx-10.5-x86_64-2.7/cephes/i0.o build/temp.macosx-10.5-x86_64-2.7/cephes/c2f.o build/temp.macosx-10.5-x86_64-2.7/cephes/chbevl.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/fortranobject.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib.o build/temp.macosx-10.5-x86_64-2.7/pymc/histogram.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib_blas.o build/temp.macosx-10.5-x86_64-2.7/pymc/blas_wrap.o build/temp.macosx-10.5-x86_64-2.7/pymc/math.o build/temp.macosx-10.5-x86_64-2.7/pymc/gibbsit.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.o -L/usr/local/Cellar/gfortran/4.7.2/gfortran/lib/gcc/x86_64-apple-darwin12.2.0/4.7.2 -lgfortran -o build/lib.macosx-10.5-x86_64-2.7/pymc/flib.so -Wl,-framework -Wl,Accelerate\" failed with exit status 1\r\n",
        "    Complete output from command //anaconda/bin/python -c \"import setuptools;__file__='/private/var/folders/6l/83vj6nxn6g1b6l2c9ycym4hc0000gn/T/pip_build_olga/pymc/setup.py';exec(compile(open(__file__).read().replace('\\r\\n', '\\n'), __file__, 'exec'))\" install --record /var/folders/6l/83vj6nxn6g1b6l2c9ycym4hc0000gn/T/pip-PlF1xm-record/install-record.txt --single-version-externally-managed:\r\n",
        "    running install\r\n",
        "\r\n",
        "running build\r\n",
        "\r\n",
        "running config_cc\r\n",
        "\r\n",
        "unifing config_cc, config, build_clib, build_ext, build commands --compiler options\r\n",
        "\r\n",
        "running config_fc\r\n",
        "\r\n",
        "unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\r\n",
        "\r\n",
        "running build_src\r\n",
        "\r\n",
        "build_src\r\n",
        "\r\n",
        "building extension \"pymc.flib\" sources\r\n",
        "\r\n",
        "f2py options: ['skip:ppnd7']\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.f' to sources.\r\n",
        "\r\n",
        "building extension \"pymc.LazyFunction\" sources\r\n",
        "\r\n",
        "building extension \"pymc.Container_values\" sources\r\n",
        "\r\n",
        "building extension \"pymc.gp.linalg_utils\" sources\r\n",
        "\r\n",
        "f2py options: []\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "\r\n",
        "building extension \"pymc.gp.incomplete_chol\" sources\r\n",
        "\r\n",
        "f2py options: []\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "\r\n",
        "building extension \"pymc.gp.cov_funs.isotropic_cov_funs\" sources\r\n",
        "\r\n",
        "f2py options: []\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs/isotropic_cov_funs-f2pywrappers.f' to sources.\r\n",
        "\r\n",
        "building extension \"pymc.gp.cov_funs.distances\" sources\r\n",
        "\r\n",
        "f2py options: []\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7/fortranobject.c' to sources.\r\n",
        "\r\n",
        "  adding 'build/src.macosx-10.5-x86_64-2.7' to include_dirs.\r\n",
        "\r\n",
        "build_src: building npy-pkg config files\r\n",
        "\r\n",
        "running build_py\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/calc_utils.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/CircularStochastic.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/CommonDeterministics.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/Container.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/datatypes.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/decorators.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/diagnostics.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/distributions.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/graph.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/InstantiationDecorators.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/Matplot.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/MCMC.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/Model.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/Node.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/NormalApproximation.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/NumpyDeterministics.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/progressbar.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/PyMCObjects.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/ScipyDistributions.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/six.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/StepMethods.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/threadpool.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "copying pymc/utils.py -> build/lib.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/base.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/hdf5.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/hdf5ea.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/no_trace.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/pickle.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/ram.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/sqlite.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "copying pymc/database/txt.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/database\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/custom_step.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/disaster_model.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/disaster_model_gof.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/disaster_model_linear.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/disaster_model_missing.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/disaster_model_null.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/gelman_bioassay.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/melanoma.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/melanoma_data.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/weibull_fit.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "copying pymc/examples/zip.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/basiscov.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/cov.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/covparams.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/MCMC.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/mean.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/mesh_choice.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/observation.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/PyMCmodel.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "copying pymc/examples/gp/realizations.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/examples/gp\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/objectmodel.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_adaptive.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_AM.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_basiscov.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_binary_step.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_container.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_convergence.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_cov.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_database.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_distributions.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_GP_MCMC.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_gradients.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_graph.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_instantiation.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_interactive.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_MCMCSampler.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_mean.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_missing.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_norm_approx.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_observation.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_realization.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_special_methods.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "copying pymc/tests/test_utils.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/tests\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/BasisCovariance.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/Covariance.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/FullRankCovariance.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/gp_submodel.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/gpplots.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/GPutils.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/Mean.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/NearlyFullRankCovariance.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/Realization.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "copying pymc/gp/step_methods.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp\r\n",
        "\r\n",
        "creating build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "copying pymc/gp/cov_funs/__init__.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "copying pymc/gp/cov_funs/bases.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "copying pymc/gp/cov_funs/brownian.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "copying pymc/gp/cov_funs/cov_utils.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "copying pymc/gp/cov_funs/nsmatern.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "copying pymc/gp/cov_funs/wrapped_distances.py -> build/lib.macosx-10.5-x86_64-2.7/pymc/gp/cov_funs\r\n",
        "\r\n",
        "running build_ext\r\n",
        "\r\n",
        "customize UnixCCompiler\r\n",
        "\r\n",
        "customize UnixCCompiler using build_ext\r\n",
        "\r\n",
        "customize Gnu95FCompiler\r\n",
        "\r\n",
        "Found executable /usr/local/bin/gfortran\r\n",
        "\r\n",
        "customize Gnu95FCompiler\r\n",
        "\r\n",
        "customize Gnu95FCompiler using build_ext\r\n",
        "\r\n",
        "building 'pymc.flib' extension\r\n",
        "\r\n",
        "compiling C sources\r\n",
        "\r\n",
        "C compiler: gcc -fno-strict-aliasing -I//anaconda/include -arch x86_64 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/usr/local/opt/zlib/include\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "creating build/temp.macosx-10.5-x86_64-2.7\r\n",
        "\r\n",
        "creating build/temp.macosx-10.5-x86_64-2.7/cephes\r\n",
        "\r\n",
        "creating build/temp.macosx-10.5-x86_64-2.7/build\r\n",
        "\r\n",
        "creating build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7\r\n",
        "\r\n",
        "creating build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "compile options: '-DNO_ATLAS_INFO=3 -Ibuild/src.macosx-10.5-x86_64-2.7 -I//anaconda/lib/python2.7/site-packages/numpy/core/include -I//anaconda/include/python2.7 -c'\r\n",
        "\r\n",
        "extra options: '-msse3'\r\n",
        "\r\n",
        "gcc: cephes/i0.c\r\n",
        "\r\n",
        "gcc: cephes/c2f.c\r\n",
        "\r\n",
        "gcc: build/src.macosx-10.5-x86_64-2.7/fortranobject.c\r\n",
        "\r\n",
        "In file included from build/src.macosx-10.5-x86_64-2.7/fortranobject.c:2:\r\n",
        "\r\n",
        "In file included from build/src.macosx-10.5-x86_64-2.7/fortranobject.h:13:\r\n",
        "\r\n",
        "In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15:\r\n",
        "\r\n",
        "In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17:\r\n",
        "\r\n",
        "In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728:\r\n",
        "\r\n",
        "//anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\r\n",
        "\r\n",
        "#warning \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\"\r\n",
        "\r\n",
        " ^\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/fortranobject.c:338:30: warning: equality comparison with extraneous parentheses [-Wparentheses-equality]\r\n",
        "\r\n",
        "        if ((fp->defs[i].func==NULL)) {\r\n",
        "\r\n",
        "             ~~~~~~~~~~~~~~~~^~~~~~\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/fortranobject.c:338:30: note: remove extraneous parentheses around the comparison to silence this warning\r\n",
        "\r\n",
        "        if ((fp->defs[i].func==NULL)) {\r\n",
        "\r\n",
        "            ~                ^     ~\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/fortranobject.c:338:30: note: use '=' to turn this equality comparison into an assignment\r\n",
        "\r\n",
        "        if ((fp->defs[i].func==NULL)) {\r\n",
        "\r\n",
        "                             ^~\r\n",
        "\r\n",
        "                             =\r\n",
        "\r\n",
        "2 warnings generated.\r\n",
        "\r\n",
        "gcc: build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c\r\n",
        "\r\n",
        "In file included from build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:18:\r\n",
        "\r\n",
        "In file included from build/src.macosx-10.5-x86_64-2.7/fortranobject.h:13:\r\n",
        "\r\n",
        "In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15:\r\n",
        "\r\n",
        "In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17:\r\n",
        "\r\n",
        "In file included from //anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728:\r\n",
        "\r\n",
        "//anaconda/lib/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\r\n",
        "\r\n",
        "#warning \"Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\"\r\n",
        "\r\n",
        " ^\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:16934:7: warning: array index 2 is past the end of the array (which contains 2 elements) [-Warray-bounds]\r\n",
        "\r\n",
        "  d = shape(x,2);\r\n",
        "\r\n",
        "      ^       ~\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:86:24: note: expanded from macro 'shape'\r\n",
        "\r\n",
        "#define shape(var,dim) var ## _Dims[dim]\r\n",
        "\r\n",
        "                       ^\r\n",
        "\r\n",
        "<scratch space>:394:1: note: expanded from here\r\n",
        "\r\n",
        "x_Dims\r\n",
        "\r\n",
        "^\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:16835:3: note: array 'x_Dims' declared here\r\n",
        "\r\n",
        "  npy_intp x_Dims[2] = {-1, -1};\r\n",
        "\r\n",
        "  ^\r\n",
        "\r\n",
        "build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.c:181:12: warning: unused function 'f2py_size' [-Wunused-function]\r\n",
        "\r\n",
        "static int f2py_size(PyArrayObject* var, ...)\r\n",
        "\r\n",
        "           ^\r\n",
        "\r\n",
        "3 warnings generated.\r\n",
        "\r\n",
        "gcc: cephes/chbevl.c\r\n",
        "\r\n",
        "compiling Fortran sources\r\n",
        "\r\n",
        "Fortran f77 compiler: /usr/local/bin/gfortran -Wall -ffixed-form -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\r\n",
        "\r\n",
        "Fortran f90 compiler: /usr/local/bin/gfortran -Wall -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\r\n",
        "\r\n",
        "Fortran fix compiler: /usr/local/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\r\n",
        "\r\n",
        "creating build/temp.macosx-10.5-x86_64-2.7/pymc\r\n",
        "\r\n",
        "compile options: '-DNO_ATLAS_INFO=3 -Ibuild/src.macosx-10.5-x86_64-2.7 -I//anaconda/lib/python2.7/site-packages/numpy/core/include -I//anaconda/include/python2.7 -c'\r\n",
        "\r\n",
        "gfortran:f77: pymc/flib.f\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 509\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 510\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 511\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 512\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 513\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 557\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 558\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 559\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 560\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 561\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 6 of line 707\r\n",
        "\r\n",
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        "\r\n",
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        "Warning: Nonconforming tab character in column 1 of line 4640\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 4642\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 4643\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 4644\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 4645\r\n",
        "\r\n",
        "Warning: Nonconforming tab character in column 1 of line 4646\r\n",
        "\r\n",
        "pymc/flib.f:316.55:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION mu_now, tau_now, alph_now, d_now, like\r",
        "\r\n",
        "\r\n",
        "                                                       1\r\n",
        "\r\n",
        "Warning: Unused variable 'd_now' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:314.42:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      INTEGER i, nx, nalph, nmu, ntau, tnx\r\n",
        "\r\n",
        "                                          1\r\n",
        "\r\n",
        "Warning: Unused variable 'tnx' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:442.71:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "\r\n",
        "                                                                       1\r\n",
        "\r\n",
        "Warning: Unused dummy argument 'gradxlike' at (1)\r\n",
        "\r\n",
        "pymc/flib.f:461.40:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        DOUBLE PRECISION like, low, high\r\n",
        "\r\n",
        "                                        1\r\n",
        "\r\n",
        "Warning: Unused variable 'high' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:458.36:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        INTEGER n, nlower, nupper, i\r\n",
        "\r\n",
        "                                    1\r\n",
        "\r\n",
        "Warning: Unused variable 'i' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:461.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        DOUBLE PRECISION like, low, high\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'like' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:461.34:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        DOUBLE PRECISION like, low, high\r\n",
        "\r\n",
        "                                  1\r\n",
        "\r\n",
        "Warning: Unused variable 'low' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:442.39:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "\r\n",
        "                                       1\r\n",
        "\r\n",
        "Warning: Unused dummy argument 'lower' at (1)\r\n",
        "\r\n",
        "pymc/flib.f:442.45:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "\r\n",
        "                                             1\r\n",
        "\r\n",
        "Warning: Unused dummy argument 'upper' at (1)\r\n",
        "\r\n",
        "pymc/flib.f:442.33:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      subroutine uniform_grad_x(x,lower,upper,n,nlower,nupper,gradxlike)\r\n",
        "\r\n",
        "                                 1\r\n",
        "\r\n",
        "Warning: Unused dummy argument 'x' at (1)\r\n",
        "\r\n",
        "pymc/flib.f:692.41:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "\r\n",
        "                                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'pdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:760.41:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "\r\n",
        "                                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'pdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:834.41:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "\r\n",
        "                                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'pdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:906.41:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "\r\n",
        "                                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'pdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:976.41:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION aa, cc, sigma, pdf\r\n",
        "\r\n",
        "                                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'pdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1186.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION factln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'factln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1185.51:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION sumx, mut, infinity, sumfact\r\n",
        "\r\n",
        "                                                   1\r\n",
        "\r\n",
        "Warning: Unused variable 'sumfact' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1185.27:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION sumx, mut, infinity, sumfact\r\n",
        "\r\n",
        "                           1\r\n",
        "\r\n",
        "Warning: Unused variable 'sumx' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1300.55:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION mu(nmu), gradlike(nmu),grad, cdf\r\n",
        "\r\n",
        "                                                       1\r\n",
        "\r\n",
        "Warning: Unused variable 'cdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1302.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION factln, gammq\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'factln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1302.36:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION factln, gammq\r\n",
        "\r\n",
        "                                    1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammq' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1301.59:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION sumx, mut, infinity, sumfact, sumcdf\r\n",
        "\r\n",
        "                                                           1\r\n",
        "\r\n",
        "Warning: Unused variable 'sumcdf' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1301.51:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION sumx, mut, infinity, sumfact, sumcdf\r\n",
        "\r\n",
        "                                                   1\r\n",
        "\r\n",
        "Warning: Unused variable 'sumfact' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1301.27:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION sumx, mut, infinity, sumfact, sumcdf\r\n",
        "\r\n",
        "                           1\r\n",
        "\r\n",
        "Warning: Unused variable 'sumx' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:1402.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:2154.40:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gradlike(n), grad\r\n",
        "\r\n",
        "                                        1\r\n",
        "\r\n",
        "Warning: Unused variable 'grad' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:2665.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:2728.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, psi\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:2793.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:2903.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:2959.29:\r\n",
        "\r",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, psi\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3021.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3396.58:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gradlike(nx), atmp, btmp, PI, glike\r\n",
        "\r\n",
        "                                                          1\r\n",
        "\r\n",
        "Warning: Unused variable 'glike' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3646.37:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, factln\r\n",
        "\r\n",
        "                                     1\r\n",
        "\r\n",
        "Warning: Unused variable 'factln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3646.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, factln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3706.37:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, factln, psi\r\n",
        "\r\n",
        "                                     1\r\n",
        "\r\n",
        "Warning: Unused variable 'factln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3706.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, factln, psi\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:3836.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION factln\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'factln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:4039.26:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION psi\r\n",
        "\r\n",
        "                          1\r\n",
        "\r\n",
        "Warning: Unused variable 'psi' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:4266.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, psi\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:4332.29:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION gammln, psi\r\n",
        "\r\n",
        "                             1\r\n",
        "\r\n",
        "Warning: Unused variable 'gammln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5184.35:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION like, factln, infinity, sump\r\n",
        "\r\n",
        "                                   1\r\n",
        "\r\n",
        "Warning: Unused variable 'factln' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5186.23:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      INTEGER i,j,n_tmp\r\n",
        "\r\n",
        "                       1\r\n",
        "\r\n",
        "Warning: Unused variable 'n_tmp' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5434.26:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION tmp\r\n",
        "\r\n",
        "                          1\r\n",
        "\r\n",
        "Warning: Unused variable 'tmp' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5488.25:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION i0\r\n",
        "\r\n",
        "                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'i0' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5481.26:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION tmp\r\n",
        "\r\n",
        "                          1\r\n",
        "\r\n",
        "Warning: Unused variable 'tmp' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5535.25:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION i0\r\n",
        "\r\n",
        "                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'i0' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f:5528.26:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION tmp\r\n",
        "\r\n",
        "                          1\r\n",
        "\r\n",
        "Warning: Unused variable 'tmp' declared at (1)\r\n",
        "\r\n",
        "pymc/flib.f: In function 'elgs':\r\n",
        "\r\n",
        "pymc/flib.f:4882:0: warning: 'k' may be used uninitialized in this function [-Wmaybe-uninitialized]\r\n",
        "\r\n",
        "pymc/flib.f: In function 'exponweib_gl':\r\n",
        "\r\n",
        "pymc/flib.f:778:0: warning: 'nc' may be used uninitialized in this function [-Wuninitialized]\r\n",
        "\r\n",
        "pymc/flib.f: In function 'exponweib_gs':\r\n",
        "\r\n",
        "pymc/flib.f:994:0: warning: 'nc' may be used uninitialized in this function [-Wuninitialized]\r\n",
        "\r\n",
        "gfortran:f77: pymc/histogram.f\r\n",
        "\r\n",
        "gfortran:f77: pymc/flib_blas.f\r\n",
        "\r\n",
        "pymc/flib_blas.f:202.25:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION PI\r\n",
        "\r\n",
        "                         1\r\n",
        "\r\n",
        "Warning: Unused variable 'pi' declared at (1)\r\n",
        "\r\n",
        "gfortran:f77: pymc/blas_wrap.f\r\n",
        "\r\n",
        "gfortran:f77: pymc/math.f\r\n",
        "\r\n",
        "pymc/math.f:396.6:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION FUNCTION DERF(X)\r\n",
        "\r\n",
        "      1\r\n",
        "\r\n",
        "Warning: 'derf' declared at (1) is also the name of an intrinsic.  It can only be called via an explicit interface or if declared EXTERNAL.\r\n",
        "\r\n",
        "pymc/math.f:417.6:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "      DOUBLE PRECISION FUNCTION DERFC(X)\r\n",
        "\r\n",
        "      1\r\n",
        "\r\n",
        "Warning: 'derfc' declared at (1) is also the name of an intrinsic.  It can only be called via an explicit interface or if declared EXTERNAL.\r\n",
        "\r\n",
        "gfortran:f77: pymc/gibbsit.f\r\n",
        "\r\n",
        "pymc/gibbsit.f:2160.5:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "  215 IF (R .GT. .5898437) GO TO 220\r\n",
        "\r\n",
        "     1\r\n",
        "\r\n",
        "Warning: Label 215 at (1) defined but not used\r\n",
        "\r\n",
        "pymc/gibbsit.f:2077.5:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "  115 IF (R .GT. .5898437) GO TO 120\r",
        "\r\n",
        "\r\n",
        "     1\r\n",
        "\r\n",
        "Warning: Label 115 at (1) defined but not used\r\n",
        "\r\n",
        "pymc/gibbsit.f:2064.5:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "   15 IF (KFLAG.GE.1) GO TO 30\r\n",
        "\r\n",
        "     1\r\n",
        "\r\n",
        "Warning: Label 15 at (1) defined but not used\r\n",
        "\r\n",
        "pymc/gibbsit.f:1204.48:\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        cutpt = empquant(original,iteracnt,qhat,work)\r\n",
        "\r\n",
        "                                                1\r\n",
        "\r\n",
        "Warning: Type mismatch in argument 'work' at (1); passed INTEGER(4) to REAL(8)\r\n",
        "\r\n",
        "gfortran:f77: build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.f\r\n",
        "\r\n",
        "/usr/local/bin/gfortran -Wall -L/usr/local/opt/zlib/lib build/temp.macosx-10.5-x86_64-2.7/cephes/i0.o build/temp.macosx-10.5-x86_64-2.7/cephes/c2f.o build/temp.macosx-10.5-x86_64-2.7/cephes/chbevl.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/fortranobject.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib.o build/temp.macosx-10.5-x86_64-2.7/pymc/histogram.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib_blas.o build/temp.macosx-10.5-x86_64-2.7/pymc/blas_wrap.o build/temp.macosx-10.5-x86_64-2.7/pymc/math.o build/temp.macosx-10.5-x86_64-2.7/pymc/gibbsit.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.o -L/usr/local/Cellar/gfortran/4.7.2/gfortran/lib/gcc/x86_64-apple-darwin12.2.0/4.7.2 -lgfortran -o build/lib.macosx-10.5-x86_64-2.7/pymc/flib.so -Wl,-framework -Wl,Accelerate\r\n",
        "\r\n",
        "Undefined symbols for architecture x86_64:\r\n",
        "\r\n",
        "  \"_PyArg_ParseTupleAndKeywords\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyCObject_AsVoidPtr\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyCapsule_AsVoidPtr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyCObject_FromVoidPtr\", referenced from:\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "      _F2PyCapsule_FromVoidPtr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyCObject_Type\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyCapsule_Check in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyComplex_FromDoubles\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyComplex_Type\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyDict_DelItemString\", referenced from:\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyDict_GetItemString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyDict_New\", referenced from:\r\n",
        "\r\n",
        "      _PyFortranObject_New in fortranobject.o\r\n",
        "\r\n",
        "      _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyDict_SetItemString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyDict_SetItemString in fortranobject.o\r\n",
        "\r\n",
        "      _PyFortranObject_New in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_Clear\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyDict_SetItemString in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_Format\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_call in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_NewException\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyErr_Occurred\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyErr_Print\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyDict_SetItemString in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_SetString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyEval_RestoreThread\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyEval_SaveThread\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyExc_AttributeError\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyExc_ImportError\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyExc_MemoryError\", referenced from:\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyExc_RuntimeError\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_call in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyExc_TypeError\", referenced from:\r\n",
        "\r\n",
        "      _fortran_call in fortranobject.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyExc_ValueError\", referenced from:\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyFloat_Type\", referenced from:\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyImport_ImportModule\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyMem_Free\", referenced from:\r\n",
        "\r\n",
        "      _fortran_dealloc in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyModule_GetDict\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyNumber_Float\", referenced from:\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyNumber_Int\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyObject_GetAttrString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyObject_IsTrue\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_constrain in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyObject_SetAttrString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyObject_Str\", referenced from:\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyObject_Type\", referenced from:\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PySequence_Check\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PySequence_GetItem\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyString_AsString\", referenced from:\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyString_ConcatAndDel\", referenced from:\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyString_FromFormat\", referenced from:\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyString_FromString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyType_IsSubtype\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyType_Type\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_Py_BuildValue\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_Py_FindMethod\", referenced from:\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_Py_InitModule4_64\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"__PyObject_New\", referenced from:\r\n",
        "\r\n",
        "      _PyFortranObject_New in fortranobject.o\r\n",
        "\r\n",
        "      _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "\r\n",
        "  \"__Py_NoneStruct\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "ld: symbol(s) not found for architecture x86_64\r\n",
        "\r\n",
        "collect2: error: ld returned 1 exit status\r\n",
        "\r\n",
        "Undefined symbols for architecture x86_64:\r\n",
        "\r\n",
        "  \"_PyArg_ParseTupleAndKeywords\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyCObject_AsVoidPtr\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyCapsule_AsVoidPtr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyCObject_FromVoidPtr\", referenced from:\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "      _F2PyCapsule_FromVoidPtr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyCObject_Type\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyCapsule_Check in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyComplex_FromDoubles\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyComplex_Type\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyDict_DelItemString\", referenced from:\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyDict_GetItemString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyDict_New\", referenced from:\r\n",
        "\r\n",
        "      _PyFortranObject_New in fortranobject.o\r\n",
        "\r\n",
        "      _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyDict_SetItemString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyDict_SetItemString in fortranobject.o\r\n",
        "\r\n",
        "      _PyFortranObject_New in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_Clear\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyDict_SetItemString in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_Format\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_call in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_NewException\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyErr_Occurred\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyErr_Print\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _F2PyDict_SetItemString in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyErr_SetString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyEval_RestoreThread\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyEval_SaveThread\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_sn_like in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_rskewnorm in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_PyExc_AttributeError\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_setattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyExc_ImportError\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyExc_MemoryError\", referenced from:\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyExc_RuntimeError\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_call in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyExc_TypeError\", referenced from:\r\n",
        "\r\n",
        "      _fortran_call in fortranobject.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyExc_ValueError\", referenced from:\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyFloat_Type\", referenced from:\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyImport_ImportModule\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyMem_Free\", referenced from:\r\n",
        "\r\n",
        "      _fortran_dealloc in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyModule_GetDict\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyNumber_Float\", referenced from:\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyNumber_Int\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyObject_GetAttrString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyObject_IsTrue\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_constrain in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyObject_SetAttrString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyObject_Str\", referenced from:\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyObject_Type\", referenced from:\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PySequence_Check\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PySequence_GetItem\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "  \"_PyString_AsString\", referenced from:\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyString_ConcatAndDel\", referenced from:\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyString_FromFormat\", referenced from:\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyString_FromString\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "      _fortran_repr in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyType_IsSubtype\", referenced from:\r\n",
        "\r\n",
        "      _int_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _double_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _string_from_pyobj in flibmodule.o\r\n",
        "\r\n",
        "      _array_from_pyobj in fortranobject.o\r\n",
        "\r\n",
        "  \"_PyType_Type\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"_Py_BuildValue\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "  \"_Py_FindMethod\", referenced from:\r\n",
        "\r\n",
        "      _fortran_getattr in fortranobject.o\r\n",
        "\r\n",
        "  \"_Py_InitModule4_64\", referenced from:\r\n",
        "\r\n",
        "      _initflib in flibmodule.o\r\n",
        "\r\n",
        "  \"__PyObject_New\", referenced from:\r\n",
        "\r\n",
        "      _PyFortranObject_New in fortranobject.o\r\n",
        "\r\n",
        "      _PyFortranObject_NewAsAttr in fortranobject.o\r\n",
        "\r\n",
        "  \"__Py_NoneStruct\", referenced from:\r\n",
        "\r\n",
        "      _f2py_rout_flib_symmetrize in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_logsum_cpx in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_combinationln in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_expand_triangular in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_mod_to_circle in flibmodule.o\r\n",
        "\r\n",
        "      _f2py_rout_flib_standardize in flibmodule.o\r\n",
        "\r\n",
        "      ...\r\n",
        "\r\n",
        "ld: symbol(s) not found for architecture x86_64\r\n",
        "\r\n",
        "collect2: error: ld returned 1 exit status\r\n",
        "\r\n",
        "error: Command \"/usr/local/bin/gfortran -Wall -L/usr/local/opt/zlib/lib build/temp.macosx-10.5-x86_64-2.7/cephes/i0.o build/temp.macosx-10.5-x86_64-2.7/cephes/c2f.o build/temp.macosx-10.5-x86_64-2.7/cephes/chbevl.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flibmodule.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/fortranobject.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib.o build/temp.macosx-10.5-x86_64-2.7/pymc/histogram.o build/temp.macosx-10.5-x86_64-2.7/pymc/flib_blas.o build/temp.macosx-10.5-x86_64-2.7/pymc/blas_wrap.o build/temp.macosx-10.5-x86_64-2.7/pymc/math.o build/temp.macosx-10.5-x86_64-2.7/pymc/gibbsit.o build/temp.macosx-10.5-x86_64-2.7/build/src.macosx-10.5-x86_64-2.7/pymc/flib-f2pywrappers.o -L/usr/local/Cellar/gfortran/4.7.2/gfortran/lib/gcc/x86_64-apple-darwin12.2.0/4.7.2 -lgfortran -o build/lib.macosx-10.5-x86_64-2.7/pymc/flib.so -Wl,-framework -Wl,Accelerate\" failed with exit status 1\r\n",
        "\r\n",
        "----------------------------------------\r\n",
        "Cleaning up...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Command //anaconda/bin/python -c \"import setuptools;__file__='/private/var/folders/6l/83vj6nxn6g1b6l2c9ycym4hc0000gn/T/pip_build_olga/pymc/setup.py';exec(compile(open(__file__).read().replace('\\r\\n', '\\n'), __file__, 'exec'))\" install --record /var/folders/6l/83vj6nxn6g1b6l2c9ycym4hc0000gn/T/pip-PlF1xm-record/install-record.txt --single-version-externally-managed failed with error code 1 in /private/var/folders/6l/83vj6nxn6g1b6l2c9ycym4hc0000gn/T/pip_build_olga/pymc\r\n",
        "Storing complete log in /Users/olga/.pip/pip.log\r\n",
        "Could not install 'pymc' using pip\r\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pymc as pm\n",
      "\n",
      "with pm.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement\n",
      "    # Define priors\n",
      "    alpha = pm.Normal('alpha', mu=0, sd=20)\n",
      "    beta = pm.Normal('beta', mu=0, sd=20)\n",
      "    sigma = pm.Uniform('sigma', lower=0, upper=20)\n",
      "    \n",
      "    # Define linear regression\n",
      "    y_est = alpha + beta * x\n",
      "    \n",
      "    # Define likelihood\n",
      "    likelihood = pm.Normal('y', mu=y_est, sd=sigma, observed=y)\n",
      "    \n",
      "    # Inference!\n",
      "    start = pm.find_MAP() # Find starting value by optimization\n",
      "    step = pm.NUTS(state=start) # Instantiate MCMC sampling algorithm\n",
      "    trace = pm.sample(2000, step, start=start, progressbar=False) # draw 2000 posterior samples using NUTS sampling"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named pymc",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-24-262be0b52243>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpymc\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# model specifications in PyMC3 are wrapped in a with-statement\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m# Define priors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: No module named pymc"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Covenience function glm()"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with pm.Model() as model:\n",
      "    # specify glm and pass in data. The resulting linear model, its likelihood and \n",
      "    # and all its parameters are automatically added to our model.\n",
      "    pm.glm.glm('y ~ x', data)\n",
      "    step = pm.NUTS() # Instantiate MCMC sampling algorithm\n",
      "    trace = pm.sample(2000, step, progressbar=False) # draw 2000 posterior samples using NUTS sampling"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Posterior"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = pm.traceplot(trace, lines={'alpha': 1, 'beta': 2, 'sigma': .5});"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10, 10))\n",
      "ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')\n",
      "ppl.scatter(ax, x, y, label='data')\n",
      "glm.plot_posterior_predictive(trace, samples=100, \n",
      "                              label='posterior predictive regression lines')\n",
      "ppl.plot(ax, x, true_regression_line, label='true regression line', linewidth=5.)\n",
      "ax.legend(loc=0);\n",
      "fig.savefig('ppc1.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('ppc1.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Robust Regression"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Add outliers\n",
      "x_out = np.append(x, [.1, .15, .2, .25, .25])\n",
      "y_out = np.append(y, [8, 6, 9, 7, 9])\n",
      "\n",
      "data_out = dict(x=x_out, y=y_out)\n",
      "fig = plt.figure(figsize=(7, 7))\n",
      "ax = fig.add_subplot(111,  xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')\n",
      "ppl.scatter(ax, x_out, y_out, label='data')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with Model() as model:\n",
      "    glm.glm('y ~ x', data_out)\n",
      "    trace = sample(2000, NUTS(), progressbar=False)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10, 10))\n",
      "ax = fig.add_subplot(111,  xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')\n",
      "ppl.scatter(ax, x_out, y_out, label='data')\n",
      "glm.plot_posterior_predictive(trace, samples=100, \n",
      "                              label='posterior predictive regression lines')\n",
      "ppl.plot(ax, x, true_regression_line, \n",
      "         label='true regression line', linewidth=5.)\n",
      "\n",
      "plt.legend(loc=0);\n",
      "fig.savefig('ppc2.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('ppc2.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111)\n",
      "normal_dist = Normal.dist(mu=0, sd=1)\n",
      "t_dist = T.dist(mu=0, lam=1, nu=1)\n",
      "x_eval = np.linspace(-8, 8, 300)\n",
      "ppl.plot(ax, x_eval, theano.tensor.exp(normal_dist.logp(x_eval)).eval(), label='Normal', linewidth=2.)\n",
      "ppl.plot(ax, x_eval, theano.tensor.exp(t_dist.logp(x_eval)).eval(), label='Student T', linewidth=2.)\n",
      "plt.xlabel('x')\n",
      "plt.ylabel('Probability density')\n",
      "plt.legend();\n",
      "fig.savefig('t-dist.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Fit strongly biased by outliers\n",
      "\n",
      "* Normal distribution has very light tails.\n",
      "* -> sensitive to outliers.\n",
      "* Instead, use Student T distribution with heavier tails."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('t-dist.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with pm.Model() as model_robust:\n",
      "    family = pm.glm.families.T()\n",
      "    pm.glm.glm('y ~ x', data_out, family=family)\n",
      "    \n",
      "    trace_robust = pm.sample(2000, pm.NUTS(), progressbar=False)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10, 10))\n",
      "ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')\n",
      "ppl.scatter(ax, x_out, y_out)\n",
      "glm.plot_posterior_predictive(trace_robust, samples=100,\n",
      "                              label='posterior predictive regression lines')\n",
      "ppl.plot(ax, x, true_regression_line, \n",
      "         label='true regression line', linewidth=5.)\n",
      "plt.legend();\n",
      "fig.savefig('ppc3.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('ppc3.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Real-world example: Algorithmic Trading\n",
      "=======================================\n",
      "\n",
      "* Pairtrading is a famous technique that plays two stocks against each other.\n",
      "* For this to work, stocks must be correlated (cointegrated).\n",
      "* One common example is the price of gold (GLD) and the price of gold mining operations (GDX)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import zipline\n",
      "import pytz\n",
      "from datetime import datetime\n",
      "fig = plt.figure(figsize=(8, 4))\n",
      "\n",
      "prices = zipline.data.load_from_yahoo(stocks=['GLD', 'GDX'], \n",
      "                                 end=datetime(2013, 8, 1, 0, 0, 0, 0, pytz.utc)).dropna()[:1000]\n",
      "prices.plot();"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(9, 6))\n",
      "ax = fig.add_subplot(111, xlabel='Price GDX in \\$', ylabel='Price GLD in \\$')\n",
      "colors = np.linspace(0.1, 1, len(prices))\n",
      "mymap = plt.get_cmap(\"winter\")\n",
      "sc = ax.scatter(prices.GDX, prices.GLD, c=colors, cmap=mymap, lw=0)\n",
      "cb = plt.colorbar(sc)\n",
      "cb.ax.set_yticklabels([str(p.date()) for p in prices[::len(prices)//10].index]);\n",
      "fig.savefig('price_corr.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('price_corr.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Naive model assumes constant linear regression."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with pm.Model() as model_reg:\n",
      "    family = pm.glm.families.Normal()\n",
      "    pm.glm.glm('GLD ~ GDX', prices, family=family)\n",
      "    trace_reg = pm.sample(2000, pm.NUTS(), progressbar=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Hm... kinda unsatisfying..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(9, 6))\n",
      "ax = fig.add_subplot(111, xlabel='Price GDX in $', ylabel='Price GLD in $', \n",
      "            title='Posterior predictive regression lines')\n",
      "sc = ax.scatter(prices.GDX, prices.GLD, c=colors, cmap=mymap, lw=0)\n",
      "glm.plot_posterior_predictive(trace_reg, samples=100, \n",
      "                              label='posterior predictive regression lines',\n",
      "                              lm=lambda x, sample: sample['Intercept'] + sample['GDX'] * x,\n",
      "                              eval=np.linspace(prices.GDX.min(), prices.GDX.max(), 100))\n",
      "cb = plt.colorbar(sc)\n",
      "cb.ax.set_yticklabels([str(p.date()) for p in prices[::len(prices)//10].index]);\n",
      "ax.legend(loc=0);\n",
      "fig.savefig('ppc4.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('ppc4.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Clearly the regression between GDX and GLD changes over time.\n",
      "* But it does so gradually.\n",
      "* Can we build a model that allows for gradual changes in the coefficients?\n",
      "* YES!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Improved model\n",
      "==============\n",
      "\n",
      "* Assumes that intercept and slope follow a **random walk**.\n",
      "* At each time-point, the coefficients can move a step from their previous values.\n",
      "* This allows the coefficients to track the regression as it changes over time.\n",
      "\n",
      "$$ \\alpha_t \\sim \\mathcal{N}(\\alpha_{t-1}, \\sigma_\\alpha^2) $$\n",
      "$$ \\beta_t \\sim \\mathcal{N}(\\beta_{t-1}, \\sigma_\\beta^2) $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymc.distributions.timeseries import *\n",
      "from theano.tensor import repeat"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## $$\\text{Priors for }\\sigma_{\\alpha}\\text{ and }\\sigma_{\\beta}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_randomwalk = pm.Model()\n",
      "with model_randomwalk:\n",
      "    # std of random walk, best sampled in log space.\n",
      "    sigma_alpha, log_sigma_alpha = model_randomwalk.TransformedVar(\n",
      "                            'sigma_alpha', \n",
      "                            pm.Exponential.dist(1./.02, testval = .1), \n",
      "                            pm.logtransform\n",
      "    )\n",
      "    sigma_beta, log_sigma_beta = model_randomwalk.TransformedVar(\n",
      "                            'sigma_beta', \n",
      "                            pm.Exponential.dist(1./.02, testval = .1),\n",
      "                            pm.logtransform\n",
      "    )"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Define regression coefficients to follow a random walk."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# To make the model simpler, we will apply the same coefficient for 50 data points at a time\n",
      "subsample_alpha = 50\n",
      "subsample_beta = 50\n",
      "\n",
      "with model_randomwalk:\n",
      "    alpha = pm.GaussianRandomWalk('alpha', sigma_alpha**-2, \n",
      "                               shape=len(prices) / subsample_alpha)\n",
      "    beta = pm.GaussianRandomWalk('beta', sigma_beta**-2, \n",
      "                              shape=len(prices) / subsample_beta)\n",
      "    \n",
      "    # Make coefficients have the same length as prices\n",
      "    alpha_r = repeat(alpha, subsample_alpha)\n",
      "    beta_r = repeat(beta, subsample_beta)    "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Define regression and likelihood"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with model_randomwalk:\n",
      "    # Define regression\n",
      "    regression = alpha_r + beta_r * prices.GDX.values\n",
      "    \n",
      "    # Assume prices are Normally distributed, the mean comes from the regression.\n",
      "    sd = pm.Uniform('sd', 0, 20)\n",
      "    likelihood = pm.Normal('y', \n",
      "                           mu=regression, \n",
      "                           sd=sd, \n",
      "                           observed=prices.GLD.values)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Inference!\n",
      "=========="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import optimize\n",
      "with model_randomwalk:\n",
      "    # First optimize random walk\n",
      "    start = pm.find_MAP(vars=[alpha, beta], fmin=optimize.fmin_l_bfgs_b)\n",
      "    \n",
      "    # Sample\n",
      "    step = pm.NUTS(scaling=start)\n",
      "    trace_rw = pm.sample(2000, step, start=start, progressbar=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# **intercept** changes over time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = plt.subplot(111, xlabel='time', ylabel='alpha', title='Change of alpha over time.')\n",
      "ppl.plot(ax, trace_rw[-1000:][alpha].T, 'r', alpha=.05);\n",
      "ax.set_xticklabels([str(p.date()) for p in prices[::len(prices)//5].index]);\n",
      "fig.savefig('rwalk_alpha.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('rwalk_alpha.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# **Slope** changes over time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111, xlabel='time', ylabel='beta', title='Change of beta over time')\n",
      "ppl.plot(ax, trace_rw[-1000:][beta].T, 'b', alpha=.05);\n",
      "ax.set_xticklabels([str(p.date()) for p in prices[::len(prices)//5].index]);\n",
      "fig.savefig('rwalk_beta.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('rwalk_beta.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Regression slowly adapts to best fit current data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 6))\n",
      "ax = fig.add_subplot(111, xlabel='Price GDX in \\$', ylabel='Price GLD in \\$', \n",
      "            title='Posterior predictive regression lines')\n",
      "\n",
      "colors = np.linspace(0.1, 1, len(prices))\n",
      "colors_sc = np.linspace(0.1, 1, len(trace_rw[-500::10]['alpha'].T))\n",
      "mymap = plt.get_cmap('winter')\n",
      "mymap_sc = plt.get_cmap('winter')\n",
      "\n",
      "xi = np.linspace(prices.GDX.min(), prices.GDX.max(), 50)\n",
      "for i, (alpha, beta) in enumerate(zip(trace_rw[-500::10]['alpha'].T, trace_rw[-500::10]['beta'].T)):\n",
      "    for a, b in zip(alpha, beta):\n",
      "        ax.plot(xi, a + b*xi, alpha=.05, lw=1, c=mymap_sc(colors_sc[i]))\n",
      "        \n",
      "sc = ax.scatter(prices.GDX, prices.GLD, label='data', cmap=mymap, c=colors)\n",
      "cb = plt.colorbar(sc)\n",
      "cb.ax.set_yticklabels([str(p.date()) for p in prices[::len(prices)//10].index]);\n",
      "fig.savefig('ppc5.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('ppc5.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Conclusions\n",
      "===========\n",
      "\n",
      "* **Probabilistic Programming** allows you to tell a **genarative story**.\n",
      "* **Blackbox inference algorithms** allow estimation of **complex models**.\n",
      "* **PyMC3** puts **advanced samplers** at your fingertips."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Outstanding Issues\n",
      "\n",
      "Scalability \n",
      "\n",
      "   - Variational Inference\n",
      "   - see also Max Welling's work for scaling MCMC\n",
      "\n",
      "Usability \n",
      "\n",
      "   - still too difficult to use\n",
      "   - wanted: library on top of PyMC3 with common models\n",
      "\n",
      "# Further reading\n",
      "\n",
      "* [Quantopian](https://www.quantopian.com) -- Develop trading algorithms like this in your browser.\n",
      "* [Probilistic Programming for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) -- IPython Notebook book on Bayesian stats using PyMC2.\n",
      "* [Doing Bayesian Data Analysis](http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/) -- Great book by Kruschke.\n",
      "* [Get PyMC3 alpha](https://github.com/pymc-devs/pymc/tree/pymc3)\n",
      "* [My blog on all things Bayesian](https://twiecki.github.io)\n",
      "* [IPython Notebook underlying this talk](https://rawgithub.com/twiecki/pymc3_talk/master/bayesian_pymc3.ipynb)\n",
      "* Twitter: [@twiecki](https://twitter.com/twiecki)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}